Palmprint gender classification by convolutional neural network

Abstract

Palmprint gender classification can revolutionise the performance of authentication systems, reduce searching space and speed up matching rate. However, to the best of their knowledge, there is no literature addressing this issue. The authors design a new convolutional neural network (CNN) structure, fine-tuning Visual Geometry Group Network, up to 19 layers to achieve a 20-layer network, for palmprint gender classification. Experimental results show that the proposed structure could achieve good performance for gender classification. They also investigate palmprint images with 15 different kinds of spectra. They empirically find that a palmprint image acquired by the Blue spectrum could achieve 89.2% correct classification and could be considered as a suitable spectrum for gender classification. The neural network is able to classify a 224 × 224 × 3-pixel palmprint image in <23 ms, verifying that the proposed CNN is an effective real-time solution.

High-Performance Server-Based Live Streaming Transmission Optimization for Sports Events in Smart Cities

“…In addition, on the video decoder side, the frame interpolation method is used to recover the previous video segment data. In particular, three-dimensional convolutional neural network (3DCNN) (a deep learning method [14,15]) is used to make the classification for these sports video segments by analyzing temporal information and spatial information of video frame sequences, in which there are three kinds of video segments, that is, radical change, gradual change, and ordinary change. In fact, 3DCNN has attracted attention for video information processing, since it introduces the time dimension innovatively on the basis of spatial dimensions to capture the contextual information between the different frames in the sports video.…”

Gender classification using custom convolutional neural networks architecture

“…Like other tasks such as the characterization of age, gender, facial attributes, expressions, and personality, automatic gender classification has various important applications such as intelligent user interfaces, user identification, social interaction, visual surveillance, collecting demographic statistics for marketing, behaviour recognition and so on. Therefore, many research efforts have been devoted to design automated system which can classify genders [1][2][3][4]. Although this task has been largely addressed in the past, the reported performances are far from optimal especially under unconstrained conditions [5,6].…”

Democratic voting downsampling for coding‐based palmprint recognition

“…The scheme is an end‐to‐end trainable, but the sub‐networks must be trained separately. Xie et al [26] used CNN with real‐time performance to classify the gender based on multispectral palmprint images. The CNN‐based methods can achieve satisfactory accuracies, but a lot of data are required to train the models.…”