Facial analysis in video : detection and recognition

Biometric authentication systems automatically identify or verify individuals using physiological (e.g., face, fingerprint, hand geometry, retina scan) or behavioral (e.g., speaking pattern, signature, keystroke dynamics) characteristics. Among these biometrics, facial patterns have the major advantage of being the least intrusive. Automatic face recognition systems thus have great potential in a wide spectrum of application areas. Focusing on facial analysis, this dissertation presents a face detection method and numerous feature extraction methods for face recognition. Concerning face detection, a video-based frontal face detection method has been developed using motion analysis and color information to derive field of interests, and distribution-based distance (DBD) and support vector machine (SVM) for classification. When applied to 92 still images (containing 282 faces), this method achieves 98.2% face detection rate with two false detections, a performance comparable to the state-of-the-art face detection methods; when applied to videQ streams, this method detects faces reliably and efficiently. Regarding face recognition, extensive assessments of face recognition performance in twelve color spaces have been performed, and a color feature extraction method defined by color component images across different color spaces is shown to help improve the baseline performance of the Face Recognition Grand Challenge (FRGC) problems. The experimental results show that some color configurations, such as YV in the YUV color space and YJ in the YIQ color space, help improve face recognition performance. Based on these improved results, a novel feature extraction method implementing genetic algorithms (GAs) and the Fisher linear discriminant (FLD) is designed to derive the optimal discriminating features that lead to an effective image representation for face recognition. This method noticeably improves FRGC ver1.0 Experiment 4 baseline recognition rate from 37% to 73%, and significantly elevates FRGC xxxx Experiment 4 baseline verification rate from 12% to 69%. Finally, four two-dimensional (2D) convolution filters are derived for feature extraction, and a 2D+3D face recognition system implementing both 2D and 3D imaging modalities is designed to address the FRGC problems. This method improves FRGC ver2.0 Experiment 3 baseline performance from 54% to 72%

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

Synthesizing Normalized Faces from Facial Identity Features

We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar

Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda