Sorry, you need to enable JavaScript to visit this website.

The task of identifying people by the way they walk is known as ‘gait recognition’. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks as independent ones, defining separated task-specific features and models for them.

In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization.

Human identification has now been a social liability due to
frequent terror threats and corrupt bureaucratic practices,
especially in rural countries like India. It has been surprisingly
observed that fingerprint quality is poor as compared
with finger knuckle quality of rural users as they exist on
the outer hand side. In this paper, we are proposing a novel
finger-knuckle-print based identification system. Initially,
finger knuckle image is preprocessed using proposed local
and adaptive image transformations. Then, finger knuckle

Human identification has now been a social liability due to
frequent terror threats and corrupt bureaucratic practices,
especially in rural countries like India. It has been surprisingly
observed that fingerprint quality is poor as compared
with finger knuckle quality of rural users as they exist on
the outer hand side. In this paper, we are proposing a novel
finger-knuckle-print based identification system. Initially,
finger knuckle image is preprocessed using proposed local
and adaptive image transformations. Then, finger knuckle

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores