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Gait recognition with a single sample per person (SSPP) is a challenging problem but has so far drawn little research attention. Inspired by similar research in face recognition, we propose to utilize the intra-class variation information learned from an additional generic training set with multiple samples per person to improve the representation of the query sample. We learn a sparse variation dictionary...
To recognize facial expression from candid, non-posed images, we propose a deep-learning based approach using convolutional neural networks (CNNs). In order to evaluate the performance in real-time candid facial expression recognition, we have created a candid image facial expression (CIFE) dataset, with seven types of expression in more than 10,000 images gathered from the Web. As baselines, two...
Shortest feature line segment (SFLS) is a recently proposed classification approach based on nearest feature line (NFL). It naturally inherits the representational capacity enlargement property of NFL and offers many other benefits in accuracy and efficiency. However, SFLS still has several drawbacks, limiting its generalization ability. In this paper, we develop a manifold learning algorithm for...
The uncorrelated discriminant linear analysis (ULDA) has been proved to be an effective feature extraction method and is known as a development of classical linear discriminant analysis (LDA). In real-world applications, we often encounter the "small sample size" (SSS) problem that the number of training samples is less than the dimension of feature vectors. Under this situation, the within-class...
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