Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Xu, Qianyi; Qin, Guihe; Sun, Minghui; Yan, Jie; Jiang, Huiming; Zhang, Zhonghan
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 6(2020)
• Page Start: 1153
• Page End: 1164
• Publication Date: 2020-11-01
• Subjects: feature extraction -- gesture recognition -- learning (artificial intelligence) -- neural nets; Depth based gesture recognition database -- Feature embedding branch -- dynamic data -- static data -- key frame samples -- improved weighted frame unification method -- feature extractor -- efficient neural network -- compact network -- uncorrelated gesture factors -- video gestures recognition -- depth videos -- automotive interfaces -- Feature fusion based hand gesture recognition method; Weighted frame unification -- Discriminant correlation analysis (DCA) -- Feature embedding branch (FEB) -- Gesture recognition -- Loss function; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.06.008
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state‐of‐the‐art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.