Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This article presents a hybrid feature extraction network to enhance the discriminative power of emotional features. The proposed network consists of a Spatial Attention Convolutional Neural Network (SACNN) and a series of Long Short-term Memory networks with Attention mechanism (ALSTMs). The SACNN is employed to extract the expressional features from static face images and the ALSTMs is designed to explore the potentials of facial landmarks for expression recognition. A deep geometric feature descriptor is proposed to characterize the relative geometric position correlation of facial landmarks. The landmarks are divided into seven groups to extract deep geometric features, and the attention module in ALSTMs can adaptively estimate the importance of different landmark regions. By jointly combining SACNN and ALSTMs, the hybrid features are obtained for expression recognition. Experiments conducted on three public databases, FER2013, CK+, and JAFFE, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 74.31%, 95.15%, and 98.57%, respectively. The preliminary results of Emotion Understanding Robot System (EURS) indicate that the proposed method has the potential to improve the performance of human-robot interaction.
Sixteen new Althepus species of the spider family Ochyroceratidae are reported from Southeast Asia: A. bamensis Li et Li sp. nov., A. changmao Li et Li sp. nov., A. dongnaiensis Li et Li sp. nov., A. guan Li et Li sp. nov., A. maechamensis Li et Li sp. nov., A. menglaensis Li et Li sp. nov., A. naphongensis Li et Li sp. nov., A. natmataungensis Li et Li sp. nov. (male only), A. phadaengensis Li et Li sp. nov., A. shanhu Li et Li sp. nov., A. suayaiensis Li et Li sp. nov. (male only), A. tadetuensis Li et Li sp. nov., A. tanhuang Li et Li sp. nov., A. thanlaensis Li et Li sp. nov., A. tharnlodensis Li et Li sp. nov. (female only), and A. viengkeoensis Li et Li sp. nov.
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