Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance. However, identifying the optimal frames that provide the most valuable input for the face anti-spoofing remains a challenging task. In this paper, we address this challenge by employing Gaussian weighting to create apex frames for videos. Specifically, an apex frame is derived from a video by computing a weighted sum of its frames, where the weights are determined using a Gaussian distribution centered around the video's central frame. Furthermore, we explore various temporal lengths to produce multiple unlabeled apex frames using a Gaussian function, without the need for convolution. By doing so, we leverage the benefits of semi-supervised learning, which considers both labeled and unlabeled apex frames to effectively discriminate between live and spoof classes. Our key contribution emphasizes the apex frame's capacity to represent the most significant moments in the video, while unlabeled apex frames facilitate efficient semi-supervised learning, as they enable the model to learn from videos of varying temporal lengths. Experimental results using four face anti-spoofing databases: CASIA, REPLAY-ATTACK, OULU-NPU, and MSU-MFSD demonstrate the apex frame's efficacy in advancing face anti-spoofing techniques.

通过使用高斯加权方法产生顶点帧以及不需要卷积的方式产生多个未标记的顶点帧，本研究重点强调顶点帧能够代表视频中最重要的时刻，同时利用未标记的顶点帧实现高效的半监督学习，使模型能够从不同的时间长度的视频中学习，并且在四个人脸反欺骗数据库的实验证实了顶点帧在促进人脸反欺骗技术方面的有效性。

使用Apex帧进行人脸反欺骗的半监督学习