In the recent past, deep learning methods have demonstrated remarkable success for
supervised learning tasks in multiple domains including computer vision, natural language …

The performance of a biometric system that relies on a single biometric modality (eg,
fingerprints only) is often stymied by various factors such as poor data quality or limited …

In the past few years, deep learning-based models have been very successful in achieving
state-of-the-art results in many tasks in computer vision, speech recognition, and natural …

This paper describes the world's largest gait database with wide view variation, the “OU-ISIR
gait database, multi-view large population dataset (OU-MVLP)”, and its application to a …

This paper proposes a method of gait recognition using a convolutional neural network
(CNN). Inspired by the great successes of CNNs in image recognition tasks, we feed in the …

The essential human gait parameters are briefly reviewed, followed by a detailed review of
the state of the art in deep learning for the human gait analysis. The modalities for capturing …

In this paper, we discuss input/output architectures for convolutional neural network (CNN)-
based cross-view gait recognition. For this purpose, we consider two aspects: verification …

Purpose This paper aims to deal with the human activity recognition using human gait
pattern. The paper has considered the experiment results of seven different activities: normal …

Gait recognition aims to recognize persons' identities by walking styles. Gait recognition has
unique advantages due to its characteristics of non-contact and long-distance compared …

The large pose discrepancy between two face images is one of the fundamental challenges
in automatic face recognition. Conventional approaches to pose-invariant face recognition …