A sample diversity and identity consistency based cross-modality model for visible-infrared person re-identification

Abstract

Visible-infrared person re-identification (VI-ReID) aims to search person images across cameras of different modalities, which can address the limitation of visible-based ReID in dark environments. It is a very challenging task, as images of the same identity have huge discrepancy in different modalities. To address this problem, a cross-modality ReID model based on sample diversity and identity consistency is proposed in this paper. For sample diversity, auxiliary images are introduced based on the idea of information transfer. The auxiliary images combine the information of visible images and infrared images, and can improve the diversity of input data and robustness of the network. For identity consistency, homogeneous distance loss and heterogeneous distance loss are developed from four different perspectives to shorten the distance between the samples of same identities. Extensive experimental results demonstrate the effectiveness of the proposed method.

Transfer learning using infrared and optical full motion video data for gender classification

“…The ODU Vision Lab has ongoing work in gender classification and person classification using optical and infrared special purpose sensors [1][2][3][4][5]. Infrared sensors compliment the abilities of visual range sensors for human subject analysis including face recognition [6][7][8][9], action recognition [10][11][12][13][14], and gender [15][16][17][18][19][20] and identity [20][21][22][23][24][25][26] recognition. These sensors may be deployed in the field with a small amount of training data available to establish recognition models.…”