Deep Regularized Discriminative Network (2021)
Journal Article
Mandal, B. (2021). Deep Regularized Discriminative Network. https://doi.org/10.1007/s42979-021-00647-z

Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We pro... Read More about Deep Regularized Discriminative 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.

Enhanced Deep Video Summarization Network (2019)
Presentation / Conference Contribution
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.

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.

Deep Adaptive Temporal Pooling for Activity Recognition (2018)
Presentation / Conference Contribution
Song, S., Cheung, N.-M., Chandrasekhar, V., & Mandal, B. (2018, October). Deep Adaptive Temporal Pooling for Activity Recognition. Presented at MM '18: ACM Multimedia Conference, Seoul Republic of Korea

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)
Presentation / Conference Contribution
Mandal, B., Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018, October). Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition. Presented at 2018 IEEE International Conference on Image Processing, Athens

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)
Presentation / Conference Contribution
Molino, A., Mandal, B., Jie, L., Lim, J.-H., Subbaraju, V., & Chandrasekhar, V. 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.

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.

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.

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.

Exploring Users Attitudes towards Social Interaction Assistance on Google Glass (2015)
Presentation / Conference Contribution
Xu, Q., Mukawa, M., Li, L., Hwee Lim, J., Tan, C., Ching Chia, S., Gan, T., & Mandal, B. (2015, March). Exploring Users Attitudes towards Social Interaction Assistance on Google Glass. Presented at 6th Augmented Human International Conference, Singapore

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.

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.

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?.