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Multi-task Cascaded Convolutional Networks for Joint Face Detection and Alignment

Face detection and alignment are crucial for various applications. This paper introduces a cascaded framework with three deep convolutional networks for accurately detecting faces and their landmarks in images. The network progresses from proposing candidate facial windows, refining them with bounding box regression, classifying faces, localizing landmarks, to a final stage for more precise supervision. Training involves face classification, bounding box regression, and landmark localization losses using specific training data. Experimental results demonstrate the effectiveness of the proposed method in achieving fast and accurate joint face detection and alignment.

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Multi-task Cascaded Convolutional Networks for Joint Face Detection and Alignment

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