Adaptive Cascade Deep Convolutional Neural Networks for face alignment

Deep convolutional network cascade has been successfully applied for face alignment. The configuration of each network, including the selecting strategy of local patches for training and the input range of local patches, is crucial for achieving desired performance. In this paper, we propose an adaptive cascade framework, termed Adaptive Cascade Deep Convolutional Neural Networks (ACDCNN) which adjusts the cascade structure adaptively. Gaussian distribution is utilized to bridge the successive networks. Extensive experiments demonstrate that our proposed ACDCNN achieves the state-of-the-art in accuracy, but with reduced model complexity and increased robustness.