Facial Attribute Recognition by Recurrent Learning With Visual Fixation
提出一种模仿人类视觉注视的循环学习方法，通过生成注视序列并输入循环网络，在表情、性别和年龄识别上显著提升准确率，优于现有静态和动态特征方法。
This paper presents a recurrent learning-based facial attribute recognition method that mimics human observers' visual fixation. The concentrated views of a human observer while focusing and exploring parts of a facial image over time are generated and fed into a recurrent network. The network makes a decision concerning facial attributes based on the features gleaned from the observer's visual fixations. Experiments on facial expression, gender, and age datasets show that applying visual fixation to recurrent networks improves recognition rates significantly. The proposed method not only outperforms state-of-the-art recognition methods based on static facial features, but also those based on dynamic facial features.