Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learning

Abstract

Effective expression analysis hugely depends upon the accurate representation of facial features. Proper identification and tracking of different facial muscles irrespective of pose, face shape, illumination, and image resolution is very much essential for serving the purpose. However, extraction and analysis of facial and appearance based features fails with improper face alignment and occlusions. Few existing works on these problems mainly determine the facial regions which contribute towards discrimination of expressions based on the training data. However, in these approaches, the positions and sizes of the facial patches vary according to the training data which inherently makes it difficult to conceive a generic system to serve the purpose. This paper proposes a novel facial landmark detection technique as well as a salient patch based facial expression recognition framework based on ACNN with significant performance at different image resolutions.

TA-DNN - Two stage Attention based Deep Neural Network for Single Image Rain Removal

“…It makes the images and videos opaque and sometimes degrades the visibility of images and videos. Most of the computer vision algorithms such as object detection, scene analysis, emotion recognition [4,10], adversarial text extractions [2,3] from images , etc. work well for input images that are clear and visible.…”

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

“…In this research, we emphasize the CNN model, which is an exemplary model for identifying persons based on their gait patterns and we intend to assess CNNs' susceptibility to adversarial attack as a primary objective. In addition, some research studies have exploited the vulnerability of the gait recognition model (Engoor, Selvaraju, Christopher, Guruvayur Suryanarayanan, & Ranganathan, 2020;Prabhu & Whaley, 2017), however, the gait representation used in their work is based on either silhouettes or accelerometric data. In contrary to these studies, this research study employs a more compact representation of gait namely GEI to investigate the vulnerability of CNN as a secondary objective.…”