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Outputs (63)

Visual Attention Assisted Games (2023)
Conference Proceeding
Mandal, B., Puhan, N. B., & Homi Anil, V. (2023). Visual Attention Assisted Games. In 2023 IEEE Conference on Games (CoG). https://doi.org/10.1109/cog57401.2023.10333186

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important... Read More about Visual Attention Assisted Games.

Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction (2023)
Conference Proceeding
Goparaju, S. U., Biju, R., M, P., MC, B., Gangadharan, D., Mandal, B., & C, P. (2023). Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction. . https://doi.org/10.1109/vtc2023-spring57618.2023.10200600

Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However,... Read More about Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction.

Deep Neural Network Based Attention Model for Structural Component Recognition (2023)
Conference Proceeding
Sarangi, S., & Mandal, B. (2023). Deep Neural Network Based Attention Model for Structural Component Recognition. . https://doi.org/10.5220/0011688400003417

The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or... Read More about Deep Neural Network Based Attention Model for Structural Component Recognition.

Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition (2022)
Journal Article
Mandal, B. (2022). Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition. Signal Processing: Image Communication, 116818 - 116818. https://doi.org/10.1016/j.image.2022.116818

Image/video based defect recognition is a crucial task in automating visual inspection of concrete structures. Although some progress has been made to automatically recognize defects in concrete structural images, significant challenges still exist.... Read More about Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition.

MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification (2022)
Journal Article
Mandal, B. (2022). MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification. https://doi.org/10.1007/s42979-022-01024-0

AbstractIn this work, we propose an attention-based deep convolutional neural network (CNN) model as an assistive computer-aided tool to classify common types of macular diseases: age-related macular degeneration, diabetic macular edema, diabetic ret... Read More about MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification.

StructureNet: Deep Context Attention Learning for Structural Component Recognition (2022)
Conference Proceeding
Kaothalkar, A., Mandal, B., & Puhan, N. (2022). StructureNet: Deep Context Attention Learning for Structural Component Recognition. . https://doi.org/10.5220/0010872800003124

Structural component recognition using images is a very challenging task due to the appearance of large components and their long continuation, existing jointly with very small components, the latter are often outcasted/missed by the existing methodo... Read More about StructureNet: Deep Context Attention Learning for Structural Component Recognition.

Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification (2021)
Journal Article
Mishra, S. S., Mandal, B., & Puhan, N. B. (2022). Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification. IEEE Transactions on Artificial Intelligence, 3(4), 625-635. https://doi.org/10.1109/tai.2021.3135797

In this article, we propose a deep architecture stemming from a perturbed composite attention mechanism with the following two novel attention modules: Multilevel perturbed spatial attention (MPSA) and multidimension attention (MDA) for macular optic... Read More about Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification.

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification. (2021)
Journal Article
Mandal, B. (2021). Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification. IEEE Transactions on Image Processing, 6957 - 6969. https://doi.org/10.1109/TIP.2021.3100556

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and... Read More about Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification..

GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment (2021)
Journal Article
Mandal, B. (2021). GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment. https://doi.org/10.1007/s42979-021-00491-1

Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method... Read More about GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment.

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification (2020)
Journal Article
Bhattacharya, G., Mandal, B., & Puhan, N. B. (2020). Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3707-3713. https://doi.org/10.1109/TCSVT.2020.3028008

In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defe... Read More about Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification.

Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification (2019)
Journal Article
Mandal, B. (2019). Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification. IEEE Signal Processing Letters, 1793-1797. https://doi.org/10.1109/LSP.2019.2949388

In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT)... Read More about Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification.

Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism (2019)
Presentation / Conference
Cook, A., Mandal, B., Berry, D., & Johnson, M. (2019, October). Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism. Paper presented at 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA

Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms an... Read More about Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism.

Enhanced Deep Video Summarization Network (2019)
Presentation / Conference
Gonuguntla, N., Mandal, B., & Puhan, N. (2019, September). Enhanced Deep Video Summarization Network. Paper presented at 30th British Machine Vision Conference, Cardiff

Video summarization is understanding video which aims to get an abstract view of the original video sequence by the concatenation of keyframes representing the highlights of the video. In this work, we propose an enhanced deep summarization network (... Read More about Enhanced Deep Video Summarization Network.

Improved Lifelog Ego-centric Video Summarization Using Ensemble of Deep Learned Object Features (2019)
Presentation / Conference
Mandal, B., & Mainwaring, P. (2019, September). Improved Lifelog Ego-centric Video Summarization Using Ensemble of Deep Learned Object Features. Presented at 30th British Machine Vision Conference, Cardiff

The ImageCLEF 2017 lifelog summarization challenge [10, 12] was established to develop a benchmark for summarizing egocentric lifelogging videos based on our daily activities, such as ‘commute to work’ or ‘cooking at home’. In this paper, we propose... Read More about Improved Lifelog Ego-centric Video Summarization Using Ensemble of Deep Learned Object Features.

Cross-spectral Periocular Recognition: a Survey (2019)
Conference Proceeding
Behera, S., Mandal, B., & Puhan, N. (2019). Cross-spectral Periocular Recognition: a Survey. In Emerging Research in Electronics, Computer Science and Technology (731–741). https://doi.org/10.1007/978-981-13-5802-9_64

Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole fac... Read More about Cross-spectral Periocular Recognition: a Survey.

Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data (2019)
Journal Article
Mandal, B., Puhan, N. B., & Verma, A. (2019). Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data. IEEE Sensors Letters, 3(2), Article ARTN 7000104. https://doi.org/10.1109/LSENS.2018.2886427

Traditional machine learning algorithms using hand-crafted feature extraction techniques (such as local binary pattern) have limited accuracy because of high variation in images of the same class (or intraclass variation) for food recognition tasks.... Read More about Deep Convolutional Generative Adversarial Network-Based Food Recognition Using Partially Labeled Data.

DeepPCA Based Objective Function for Melanoma Detection (2018)
Conference Proceeding
Sultana, N. N., Puhan, N. B., & Mandal, B. (2018). DeepPCA Based Objective Function for Melanoma Detection. In 2018 International Conference on Information Technology (ICIT). https://doi.org/10.1109/icit.2018.00025

In this paper, we propose an objective function for the convolutional neural network to acquire the variation separability as opposed to the categorical cross entropy which maximizes according to the target labels. This approach is an unsupervised le... Read More about DeepPCA Based Objective Function for Melanoma Detection.

Deep Residual Network with Regularized Fisher Framework for Detection of Melanoma (2018)
Journal Article
Mandal, B. (2018). Deep Residual Network with Regularized Fisher Framework for Detection of Melanoma. IET Computer Vision, 1096-1104. https://doi.org/10.1049/iet-cvi.2018.5238

Of all the skin cancer that is prevalent, melanoma has the highest mortality rates. Melanoma becomes life threatening when it penetrates deep into the dermis layer unless detected at an early stage, it becomes fatal since it has a tendency to migrate... Read More about Deep Residual Network with Regularized Fisher Framework for Detection of Melanoma.

Deep Adaptive Temporal Pooling for Activity Recognition (2018)
Conference Proceeding
Song, S., Cheung, N., Chandrasekhar, V., & Mandal, B. (2018). Deep Adaptive Temporal Pooling for Activity Recognition. . https://doi.org/10.1145/3240508.3240713

Deep neural networks have recently achieved competitive accuracy for human activity recognition. However, there is room for improvement, especially in modeling of long-term temporal importance and determining the activity relevance of different tempo... Read More about Deep Adaptive Temporal Pooling for Activity Recognition.

Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition (2018)
Conference Proceeding
Mandal, B., Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition. . https://doi.org/10.1109/ICIP.2018.8451190

In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior a... Read More about Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition.

I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization (2017)
Conference Proceeding
Molino, A., Mandal, B., Jie, L., Lim, J., Subbaraju, V., & Chandrasekhar, V. (2017). I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization.

In this paper we describe our approach for the ImageCLEF-lifelog summarization task. A total of ten runs were submitted, which used only visual features, only metadata information, or both. In the first step, a set of relevant frames are drawn from t... Read More about I2R VC @ ImageClef2017: Ensemble of Deep Learnt Features for Lifelog Video Summarization.

An empirical approach for automatic face clustering on personal lifelogging images (2017)
Conference Proceeding
Subbaraju, V., Xu, Q., Mandal, B., Li, L., & Lim, J. (2017). An empirical approach for automatic face clustering on personal lifelogging images. . https://doi.org/10.1109/siprocess.2017.8124519

Life-logging applications generate a vast amount of personalized data that provides vital insights into the user's daily life. One such key insight is the people whom the user has come across/interacted with during regular life. This can be obtained... Read More about An empirical approach for automatic face clustering on personal lifelogging images.

Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance (2017)
Conference Proceeding
Ragab Sayed, M., Yuting Lim, R., Mandal, B., Li, L., Hwee Lim, J., & Sim, T. (2017). Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance. In No. 7: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence

Researchers have conducted many studies on human attentions and their eye gaze patterns for face recognition (FR), hoping to inspire new ideas to develop computer vision (CV) algorithms which perform like or even better than human. Yet, while these s... Read More about Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance.

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics (2017)
Conference Proceeding
Mandal, B., Lee, D., & Ouarti, N. (2017). Distinguishing Posed and Spontaneous Smiles by Facial Dynamics. In Computer Vision – ACCV 2016 Workshops (552-566). https://doi.org/10.1007/978-3-319-54407-6_37

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features... Read More about Distinguishing Posed and Spontaneous Smiles by Facial Dynamics.

Spontaneous Versus Posed Smiles—Can We Tell the Difference? (2016)
Conference Proceeding
Mandal, B., & Ouarti, N. (2017). Spontaneous Versus Posed Smiles—Can We Tell the Difference?. . https://doi.org/10.1007/978-981-10-2107-7_24

Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways. Generally, it shows happy state of the mind, however, ‘smiles’ can be deceptive, for example people can give a smile when they feel happy an... Read More about Spontaneous Versus Posed Smiles—Can We Tell the Difference?.

Vision and Memory: Looking Beyond Immediate Visual Perception (2016)
Book Chapter
Tan, C., Lallee, S., & Mandal, B. (2017). Vision and Memory: Looking Beyond Immediate Visual Perception. In Computational and Cognitive Neuroscience of Vision (195-219). Springer. https://doi.org/10.1007/978-981-10-0213-7_9

The topic of vision is “often studied as if our conscious experience were the ultimate end-product of visual processing” (Hayhoe 2009), with research sometimes overly focused on immediate visual perception—processes aimed at understanding the current... Read More about Vision and Memory: Looking Beyond Immediate Visual Perception.

SocioGlass: Social interaction assistance with face recognition on google glass (2016)
Journal Article
Mandal, B. (2016). SocioGlass: Social interaction assistance with face recognition on google glass. https://doi.org/10.1186/s41070-016-0011-8

We present SocioGlass - a system built on Google Glass paired with a mobile phone that provides a user with in-situ information about an acquaintance in face-to-face communication. The system can recognize faces from the live feed of visual input. Ac... Read More about SocioGlass: Social interaction assistance with face recognition on google glass.

Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition (2016)
Conference Proceeding
Song, S., Chandrasekhar, V., Mandal, B., Li, L., Lim, J., Babu, G. S., …Cheung, N. (2016). Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition. . https://doi.org/10.1109/cvprw.2016.54

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to le... Read More about Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition.

MedHelp: Enhancing medication compliance for demented elderly people with wearable visual intelligence (2016)
Journal Article
Mandal, B. (2016). MedHelp: Enhancing medication compliance for demented elderly people with wearable visual intelligence. https://doi.org/10.1186/s41070-016-0006-5

Dementia results in much stress in senior citizens and and immensely affects their quality of life. It also incurs huge financial and emotional burdens to their family members. Personal information assistance may alleviate such a problem by enhancing... Read More about MedHelp: Enhancing medication compliance for demented elderly people with wearable visual intelligence.

Trends in Machine and Human Face Recognition (2016)
Book Chapter
Mandal, B., Lim, R. Y., Dai, P., Sayed, M. R., Li, L., & Lim, J. H. (2016). Trends in Machine and Human Face Recognition. In Advances in Face Detection and Facial Image Analysis (145-187). Springer. https://doi.org/10.1007/978-3-319-25958-1_7

Face recognition (FR) is a natural and intuitive way for human beings to identify or verify or at least get familiar and interact with other members of the community. Hence, human beings expect and endeavor to develop similar competency in machine re... Read More about Trends in Machine and Human Face Recognition.

Egocentric activity recognition with multimodal fisher vector (2016)
Conference Proceeding
Song, S., Cheung, N., Chandrasekhar, V., Mandal, B., & Liri, J. (2016). Egocentric activity recognition with multimodal fisher vector. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2016.7472171

With the increasing availability of wearable devices, research on egocentric activity recognition has received much attention recently. In this paper, we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data... Read More about Egocentric activity recognition with multimodal fisher vector.

Whole space subclass discriminant analysis for face recognition (2015)
Conference Proceeding
Mandal, B., Li, L., Chandrasekhar, V., & Lim, J. H. (2015). Whole space subclass discriminant analysis for face recognition. . https://doi.org/10.1109/icip.2015.7350814

In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analys... Read More about Whole space subclass discriminant analysis for face recognition.

Improved Eigenfeature Regularization for Face Identification (2015)
Presentation / Conference
Mandal, B. (2015, September). Improved Eigenfeature Regularization for Face Identification. Paper presented at ICIP 2015 - IEEE International Conference on Image Processing

In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analys... Read More about Improved Eigenfeature Regularization for Face Identification.

Enhancing Social Interaction with Seamless Face Recognition on Google Glass: Leveraging opportunistic multi-tasking on smart phones (2015)
Presentation / Conference
Chia, S., Mandal, B., Xu, Q., Li, L., & Lim, J. (2015, August). Enhancing Social Interaction with Seamless Face Recognition on Google Glass: Leveraging opportunistic multi-tasking on smart phones. Poster presented at 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, Copenhagen, Denmark

Wearable devices offer immense opportunities in both consumer and enterprise domains due to the hands-free interaction modality and the ability to provide information in real-time. However, due to hardware limitations, it presents a notable challenge... Read More about Enhancing Social Interaction with Seamless Face Recognition on Google Glass: Leveraging opportunistic multi-tasking on smart phones.

Evaluating Human Performance in Dynamic Perspective Invariant Face Recognition (2015)
Presentation / Conference
Lim, R., Ragab Sayed, M., Mandal, B., Teck Ma, K., Li, L., & Hwee Lim, J. (2015, July). Evaluating Human Performance in Dynamic Perspective Invariant Face Recognition. Paper presented at 11th Asia-Pacific Conference on Vision (APCV), Singapore

The aim of this study is to investigate and derive plausible consistent eye gaze scan path, and set of facial features learnt from unfamiliar faces in unconstrained dynamic motion (rigid and non-rigid motion) for subsequent recognition tasks using ps... Read More about Evaluating Human Performance in Dynamic Perspective Invariant Face Recognition.

Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification (2015)
Conference Proceeding
Mandal, B., Zhikai, W., Li, L., & Kassim, A. A. (2015). Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification. In Computer Vision - ACCV 2014 Workshops (585-599). https://doi.org/10.1007/978-3-319-16628-5_42

Face identification (FI) has made significant amount of progress in the last three decades. Its application is now moving towards wearable devices (like Google Glass and mobile devices) leading to the problem of FI on first-person-views (FPV) or ego-... Read More about Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification.

Exploring Users Attitudes towards Social Interaction Assistance on Google Glass (2015)
Conference Proceeding
Xu, Q., Mukawa, M., Li, L., Hwee Lim, J., Tan, C., Ching Chia, S., …Mandal, B. (2015). Exploring Users Attitudes towards Social Interaction Assistance on Google Glass. . https://doi.org/10.1145/2735711.2735831

Wearable vision brings about new opportunities for augmenting humans in social interactions. However, along with it comes privacy concerns and possible information overload. We explore users' needs and attitudes toward augmented interaction in face-t... Read More about Exploring Users Attitudes towards Social Interaction Assistance on Google Glass.

A Wearable Face Recognition System on Google Glass for Assisting Social Interactions (2015)
Conference Proceeding
Mandal, B., Chia, S., Li, L., Chandrasekhar, V., Tan, C., & Lim, J. (2015). A Wearable Face Recognition System on Google Glass for Assisting Social Interactions. In Image Processing, Computer Vision, Pattern Recognition, and Graphics (419-433). https://doi.org/10.1007/978-3-319-16634-6_31

In this paper, we present a wearable face recognition (FR) system on Google Glass (GG) to assist users in social interactions. FR is the first step towards face-to-face social interactions. We propose a wearable system on GG, which acts as a social i... Read More about A Wearable Face Recognition System on Google Glass for Assisting Social Interactions.

Efficient Retrieval from Large-Scale Egocentric Visual Data Using a Sparse Graph Representation (2014)
Conference Proceeding
Min, W., Li, X., Tan, C., Mandal, B., Li, L., & Lim, J. H. (2014). Efficient Retrieval from Large-Scale Egocentric Visual Data Using a Sparse Graph Representation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2014.84

We propose representing one's visual experiences (captured as a series of ego-centric videos) as a sparse-graph, where each node is an individual frame in the video, and nodes are connected if there exists a geometric transform between them. Such a g... Read More about Efficient Retrieval from Large-Scale Egocentric Visual Data Using a Sparse Graph Representation.

Markerless Video Analysis for Movement Quantification in Pediatric Epilepsy Monitoring (2011)
Conference Proceeding
Lu, H., Eng, H., Mandal, B., Chan, D. W., & Ng, Y. (2011). Markerless Video Analysis for Movement Quantification in Pediatric Epilepsy Monitoring. . https://doi.org/10.1109/IEMBS.2011.6092040

This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various f... Read More about Markerless Video Analysis for Movement Quantification in Pediatric Epilepsy Monitoring.

Kernel Fisher Discriminant Analysis in Full Eigenspace (2008)
Book Chapter
Mandal. (2008). Kernel Fisher Discriminant Analysis in Full Eigenspace. In Proceedings of the 2007 International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2007, June 25-28, 2007, Las Vegas Nevada, (235-241)

This work proposes a method which enables us to perform kernel Fisher discriminant analysis in the whole eigenspace for face recognition. It employs the ratio of eigenvalues to decompose the entire kernel feature space into two subspaces: a reliable... Read More about Kernel Fisher Discriminant Analysis in Full Eigenspace.

Improved Bayesian Approach for Face Recognition (2005)
Conference Proceeding
Jiang, X., Mandal, B., & Kot, A. (2005). Improved Bayesian Approach for Face Recognition. In 2005 5th International Conference on Information Communications & Signal Processing, Bangkok, 2005 (162-166). https://doi.org/10.1109/ICICS.2005.1689026

In subspace face recognition, PCA, LDA and Bayesian are the most commonly used methods. Each of them has their own advantages and disadvantages in recognizing human faces. Their recognition rates depend much on the methodologies used in selecting/tra... Read More about Improved Bayesian Approach for Face Recognition.

Face recognition: Perspectives from the real world
Conference Proceeding
Mandal, B. (2016). Face recognition: Perspectives from the real world. . https://doi.org/10.1109/icarcv.2016.7838675

In this paper, we analyze some of our real-world deployment of face recognition (FR) systems for various applications and discuss the gaps between expectations of the user and what the system can deliver. We evaluate some of the existing algorithms w... Read More about Face recognition: Perspectives from the real world.

Learning cognitive manifolds of faces
Conference Proceeding
Li, L., Mandal, B., Tan, C., & Lim, J. (2017). Learning cognitive manifolds of faces. . https://doi.org/10.1109/siprocess.2017.8124584

Inspired by the studies in psychology and neuroscience, we propose a computational model of cognitive face representation that mimics the mechanism of human face perception. We propose to learn two separate manifolds for facial identity and facial ex... Read More about Learning cognitive manifolds of faces.

Spontaneous vs. Posed smiles - can we tell the difference?
Presentation / Conference
Mandal. Spontaneous vs. Posed smiles - can we tell the difference?. Presented at International Conference on Computer Vision and Image Processing

Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways. Generally, it shows happy state of the mind, however, ‘smiles’ can be deceptive, for example people can give a smile when they feel happy an... Read More about Spontaneous vs. Posed smiles - can we tell the difference?.