We present a new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data. The proposed DDLCN has most of the standard deep learning layers (e.g., input/output, pooling, and fully connected), but the fundamental convolutional layers are replaced by our proposed compound dictionary learning and coding layers. The dictionary learning learns an overcomplete dictionary for input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Then, the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components shared among the input dictionary atoms; thus, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare DDLCN with several leading dictionary learning methods and deep learning models. Experimental results on five popular data sets show that DDLCN achieves competitive results compared with state-of-the-art methods when the training data are limited. Code is available at https://github.com/Ha0Tang/DDLCN.

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional layers are replaced by novel compound dictionary learning and coding layers. The dictionary learning layer learns an over-complete dictionary for the input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Next, the activated dictionary atoms are assembled together and passed to the next compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components which are shared among the input dictionary atoms. In this way, a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare the proposed DDLCN with several dictionary learning methods and deep learning architectures. The experimental results on four popular benchmark datasets demonstrate that the proposed DDLCN achieves competitive results compared with state-of-the-art approaches.

This paper presents a pioneering study on gender classification from unconstrained back-facing video sequences in natural scenes. In many cases, classifying gender simply via faces or other biometric cues may fail when the video only contains back-facing people. To address this problem, we propose a novel approach to classify the gender according to back-facing video sequences. For this task, a novel Pyramid Segmentation approach is proposed to divide video sequence into a suite of equal time-length sleeves with different scales. Moreover, a heuristic approach is used to compute weights for different features from each sleeve. Finally, a framework of gender classification based on video sequences is presented. To validate our approach, we introduce a new dataset, called BackFacing dataset, featured by 720 annotated back-facing human video sequences. To our knowledge, this is the first dataset only containing back-facing video shots. Experiments demonstrate that the proposed approach achieves competitive results on VidTIMIT, Cohn-Kanade, CASIA Gait and BackFacing datasets.