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In this paper, we present a face detector based on Cascade Deformable Part Models (CDPM) [1]. Our model is learnt from partially labelled images using Latent Support Vector Machines (LSVM). Recently Zhu et al. [2] proposed a Tree Structure Model for multi-view face detection trained with facial landmark labels, which resulted on a complex and suboptimal system for face detection. Instead, we adopt CDPMs enhanced with a data-mining procedure to enrich models during the LSVM training. Furthermore, a post-optimization procedure is derived to improve the performance of the CDPMs. Experimental results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the FDDB [3] and the AFLW [4] databases.

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Detecting and tracking human faces in video sequences is useful in a number of applications such as gesture recognition and human-machine interaction. In this paper, we show that online appearance models (holistic approaches) can be used for simultaneously tracking the head, the lips, the eyebrows, and the eyelids in monocular video sequences. Unlike previous approaches to eyelid tracking, we show that the online appearance models can be used for this purpose. Neither color information nor intensity edges are used by our proposed approach. More precisely, we show how the classical appearance-based trackers can be upgraded in order to deal with fast eyelid movements. The proposed eyelid tracking is made robust by avoiding eye feature extraction. Experiments on real videos show the usefulness of the proposed tracking schemes as well as their enhancement to our previous approach.

Psychological evidence has emphasized the importance of eye gaze analysis in human computer interaction and emotion interpretation. To this end, current image analysis algorithms take into consideration eye-lid and iris motion detection using colour information and edge detectors. However, eye movement is fast and and hence difficult to use to obtain a precise and robust tracking. Instead, our method proposed to describe eyelid and iris movements as continuous variables using appearance-based tracking. This approach combines the strengths of adaptive appearance models, optimization methods and backtracking techniques. Thus, in the proposed method textures are learned on-line from near frontal images and illumination changes, occlusions and fast movements are managed. The method achieves real-time performance by combining two appearance-based trackers to a backtracking algorithm for eyelid estimation and another for iris estimation. These contributions represent a significant advance towards a reliable gaze motion description for HCI and expression analysis, where the strength of complementary methodologies are combined to avoid using high quality images, colour information, texture training, camera settings and other time-consuming processes.