Predicting face attributes from web images is challenging due to background clutters and face variations. A novel deep learning framework is proposed for face attribute prediction in the wild. It cascades two CNNs (LNet and ANet) for face localization and attribute prediction respectively. These nets are trained in a cascade manner with attribute labels, but pre-trained differently. LNet is pre-trained with massive general object categories, while ANet is pre-trained with massive face identities. This framework not only outperforms state-of-the-art with large margin, but also reveals multiple valuable facts on learning face representation as below. (1) It shows how LNet and ANet can be improved by different pre-training strategies. (2) It reveals that although filters of LNet are fine-tuned by attribute labels, their response maps over the entire image have strong indication of face's location. This fact enables training LNet for face localization with only attribute tags, but without face bounding boxes (which are required by all detection works). With a novel fast feed-forward scheme, the cascade of LNet and ANet can localize faces and recognize attributes in images with arbitrary sizes in real time. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training, and such concepts are significantly enriched after fine-tuning. Each attribute can be well explained by a sparse linear combination of these concepts. By analyzing such combinations, attributes show clear grouping patterns, which could be well interpreted semantically.
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