Face recognition using multiple features in different color spaces

Face recognition as a particular problem of pattern recognition has been attracting substantial attention from researchers in computer vision, pattern recognition, and machine learning. The recent Face Recognition Grand Challenge (FRGC) program reveals that uncontrolled illumination conditions pose grand challenges to face recognition performance. Most of the existing face recognition methods use gray-scale face images, which have been shown insufficient to tackle these challenges. To overcome this challenging problem in face recognition, this dissertation applies multiple features derived from the color images instead of the intensity images only. First, this dissertation presents two face recognition methods, which operate in different color spaces, using frequency features by means of Discrete Fourier Transform (DFT) and spatial features by means of Local Binary Patterns (LBP), respectively. The DFT frequency domain consists of the real part, the imaginary part, the magnitude, and the phase components, which provide the different interpretations of the input face images. The advantage of LBP in face recognition is attributed to its robustness in terms of intensity-level monotonic transformation, as well as its operation in the various scale image spaces. By fusing the frequency components or the multi-resolution LBP histograms, the complementary feature sets can be generated to enhance the capability of facial texture description. This dissertation thus uses the fused DFT and LBP features in two hybrid color spaces, the RIQ and the VIQ color spaces, respectively, for improving face recognition performance. Second, a method that extracts multiple features in the CID color space is presented for face recognition. As different color component images in the CID color space display different characteristics, three different image encoding methods, namely, the patch-based Gabor image representation, the multi-resolution LBP feature fusion, and the DCT-based multiple face encodings, are presented to effectively extract features from the component images for enhancing pattern recognition performance. To further improve classification performance, the similarity scores due to the three color component images are fused for the final decision making. Finally, a novel image representation is also discussed in this dissertation. Unlike a traditional intensity image that is directly derived from a linear combination of the R, G, and B color components, the novel image representation adapted to class separability is generated through a PCA plus FLD learning framework from the hybrid color space instead of the RGB color space. Based upon the novel image representation, a multiple feature fusion method is proposed to address the problem of face recognition under the severe illumination conditions. The aforementioned methods have been evaluated using two large-scale databases, namely, the Face Recognition Grand Challenge (FRGC) version 2 database and the FERET face database. Experimental results have shown that the proposed methods improve face recognition performance upon the traditional methods using the intensity images by large margins and outperform some state-of-the-art methods

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database

PersonRank: Detecting Important People in Images

Always, some individuals in images are more important/attractive than others in some events such as presentation, basketball game or speech. However, it is challenging to find important people among all individuals in images directly based on their spatial or appearance information due to the existence of diverse variations of pose, action, appearance of persons and various changes of occasions. We overcome this difficulty by constructing a multiple Hyper-Interaction Graph to treat each individual in an image as a node and inferring the most active node referring to interactions estimated by various types of clews. We model pairwise interactions between persons as the edge message communicated between nodes, resulting in a bidirectional pairwise-interaction graph. To enrich the personperson interaction estimation, we further introduce a unidirectional hyper-interaction graph that models the consensus of interaction between a focal person and any person in a local region around. Finally, we modify the PageRank algorithm to infer the activeness of persons on the multiple Hybrid-Interaction Graph (HIG), the union of the pairwise-interaction and hyperinteraction graphs, and we call our algorithm the PersonRank. In order to provide publicable datasets for evaluation, we have contributed a new dataset called Multi-scene Important People Image Dataset and gathered a NCAA Basketball Image Dataset from sports game sequences. We have demonstrated that the proposed PersonRank outperforms related methods clearly and substantially.Comment: 8 pages, conferenc