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Register a new userMultiple Face Analyses through Adversarial Learning
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Added by
Shi Yin
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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This inherent relations among multiple face analysis tasks, such as landmark detection, head pose estimation, gender recognition and face attribute estimation are crucial to boost the performance of each task, but have not been thoroughly explored since typically these multiple face analysis tasks are handled as separate tasks. In this paper, we propose a novel deep multi-task adversarial learning method to localize facial landmark, estimate head pose and recognize gender jointly or estimate multiple face attributes simultaneously through exploring their dependencies from both image representation-level and label-level. Specifically, the proposed method consists of a deep recognition network R and a discriminator D. The deep recognition network is used to learn the shared middle-level image representation and conducts multiple face analysis tasks simultaneously. Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks, such as facial landmark localization, head pose estimation, gender recognition and face attribute estimation from image representation-level. The discriminator is introduced to enforce the distribution of the multiple face analysis tasks to converge to that inherent in the ground-truth labels. During training, the recognizer tries to confuse the discriminator, while the discriminator competes with the recognizer through distinguishing the predicted label combination from the ground-truth one. Though adversarial learning, we explore the dependencies among multiple face analysis tasks from label-level. Experimental results on four benchmark databases, i.e., the AFLW database, the Multi-PIE database, the CelebA database and the LFWA database, demonstrate the effectiveness of the proposed method for multiple face analyses.
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Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy-to-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation-efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy ($85.8%$ on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance ($97.3%$) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.
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