文献类型：期刊文献

英文题名：FSKT-GE: Feature maps similarity knowledge transfer for low-resolution gaze estimation

作者：Yan, Chao[1,2];Pan, Weiguo[1,2];Dai, Songyin[1,2];Xu, Bingxin[1,2];Xu, Cheng[1,2];Liu, Hongzhe[1,2];Li, Xuewei[1,2]

第一作者：Yan, Chao

通讯作者：Dai, SY[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Inst Brain & Cognit Sci, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2024

卷号：18

期号：6

起止页码：1642-1654

外文期刊名：IET IMAGE PROCESSING

收录：;EI(收录号：20240815619237);Scopus(收录号：2-s2.0-85185691931);WOS:【SCI-EXPANDED(收录号:WOS:001163861600001)】；

基金：This research was funded by the Beijing Natural Science Foundation (4232026), the National Natural Science Foundation of China (Grant No. 61906017, 62102033, 62171042, 61871028, 62272049, 62006020), the Beijing Municipal Commission of Education Project (No. KM201911417001, KM202111417001), the Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions (No. BPHR20220121), the Beijing Advanced Talents Great Wall Scholar Training Program (CIT&TCD20190313), the R & D Program of the Beijing Municipal Education Commission (KZ202211417048), and the Collaborative Innovation Center of Chaoyang (Grant No. CYXC2203). Scientific research projects of Beijing Union University (ZK10202202, BPHR2020DZ02, ZK40202101, ZK120202104).

语种：英文

外文关键词：computer graphics; computer vision; convolutional neural nets

摘要：The limited of texture details information in low-resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT-GE (feature maps similarity knowledge transfer for low-resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low-resolution datasets derived from Gaze360 and RT-Gene datasets is validated, demonstrating excellent performance in low-resolution gaze estimation. Evaluations on low-resolution images obtained through 2x, 4x, and 8x down-sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97 degrees, 11.22 degrees, and 13.61 degrees were achieved, while on the RT-Gene dataset, the lowest mean angular errors of 6.73 degrees, 6.83 degrees, and 7.75 degrees were obtained. Here, a novel approach called feature map similarity-based knowledge transfer for low-resolution gaze estimation (FSKT-GE) is proposed. The motivation behind this work is to address the challenge of accurately estimating gaze direction for low-resolution facial images encountered in unconstrained outdoor environments. image