conferences & journals

2021

2016

1. INDICON

   Adding CNNs to the Mix: Stacking models for sentiment classification

   D Vishwanath, and Shubham Gupta

   In 2016 IEEE Annual India Conference (INDICON) 2016

2013

1. ICIP

   Kitchen activity recognition based on scene context

   Shubham Bansal, Shubham Khandelwal, Shubham Gupta, and Dushyant Goyal

   In IEEE International Conference on Image Processing 2013
2. ICB

   The 2nd competition on counter measures to 2D face spoofing attacks

   I. Chingovska, J. Yang, Z. Lei, D. Yi, S. Z. Li, O. Kahm, C. Glaser, N. Damer, A. Kuijper, A. Nouak, J. Komulainen, T. Pereira, S. Gupta, S. Khandelwal, S. Bansal, A. Rai, T. Krishna, D. Goyal, M.-A. Waris, H. Zhang, I. Ahmad, S. Kiranyaz, M. Gabbouj, R. Tronci, M. Pili, N. Sirena, F. Roli, J. Galbally, J. Ficrrcz, A. Pinto, H. Pedrini, W. S. Schwartz, A. Rocha, A. Anjos, and S. Marcel

   In International Conference on Biometrics (ICB) 2013
3. ICMI

   Emotion Recognition Using Facial and Audio Features

   Tarun Krishna, Ayush Rai, Shubham Bansal, Shubham Khandelwal, Shubham Gupta, and Dushyant Goyal

   In Proceedings of the 15th ACM on International Conference on Multimodal Interaction 2013

   Abs

   Human Computer Interaction is an upcoming scientific field which aims at inter-communication between humans and computers. A major element of this field is Human Emotion Recognition. The most expressive way humans display emotions is through facial expressions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly worthwhile at the time, such lab controlled data poorly represents the environment and conditions faced in real-world situations. With the increase in the number of video clips online, it is worthwhile to explore the performance of emotion recognition methods that work ’in the wild’ .This work mainly focuses on automatic emotion recognition in a wild video sample. In this task, we have worked on the problem of human emotion recognition using a combination of video features and audio features. The technique that we have utilized for emotion detection involves a blend of Optical flow, Gabor Filtering, few other facial features and audio features. Training and Classification is performed using Support Vector Machine-Hidden Markov Model (HMM). The unique thing about our methodology is that it produces better results for some particular class of emotions as compared to the baseline score in the case of wild emotion dataset with an overall accuracy of 20.51% on the test set.