Joint Face Detection and Initialization for Face Alignment

This paper presents a joint face detection and initialization method for cascaded face alignment. Unlike existing methods which consider face detection and initialization as separate steps, we concurrently obtain a bounding box and initial facial landmarks (i.e. shape) in one step, yielding better accuracy and efficiency. Specifically, each image region is represented using shape-indexed features [6] derived from different head poses. A multipose face detector is trained: regions whose shapes are roughly aligned with faces can have a good feature representation and are utilized as positive samples, otherwise are considered as negative samples. During the face detection phase, initial landmarks can be explicitly placed on the detected faces according to the corresponding shape-indexed features. To accelerate our method, an ultrafast face proposal method based on face probability map (FPM) and boosted classifiers. Experimental results on public datasets demonstrate superior efficiency and robustness to existing initialization schemes and great accuracy improvement for the cascaded face alignment.