Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.Comment: Accepted in AAAI-1

Adaptive 3D facial action intensity estimation and emotion recognition

Automatic recognition of facial emotion has been widely studied for various computer vision tasks (e.g. health monitoring, driver state surveillance and personalized learning). Most existing facial emotion recognition systems, however, either have not fully considered subject-independent dynamic features or were limited to 2D models, thus are not robust enough for real-life recognition tasks with subject variation, head movement and illumination change. Moreover, there is also lack of systematic research on effective newly arrived novel emotion class detection. To address these challenges, we present a real-time 3D facial Action Unit (AU) intensity estimation and emotion recognition system. It automatically selects 16 motion-based facial feature sets using minimal-redundancy–maximal-relevance criterion based optimization and estimates the intensities of 16 diagnostic AUs using feedforward Neural Networks and Support Vector Regressors. We also propose a set of six novel adaptive ensemble classifiers for robust classification of the six basic emotions and the detection of newly arrived unseen novel emotion classes (emotions that are not included in the training set). A distance-based clustering and uncertainty measures of the base classifiers within each ensemble model are used to inform the novel class detection. Evaluated with the Bosphorus 3D database, the system has achieved the best performance of 0.071 overall Mean Squared Error (MSE) for AU intensity estimation using Support Vector Regressors, and 92.2% average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers. In comparison with other related work, our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database. Moreover, in on-line real-time evaluation with real human subjects, the proposed system also shows superior real-time performance with 84% recognition accuracy and great flexibility and adaptation for newly arrived novel (e.g. ‘contempt’ which is not included in the six basic emotions) emotion detection