Jun Bao Li

Bio: Jun Bao Li is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topic(s): Kernel method & Nonlinear dimensionality reduction. The author has an hindex of 1, co-authored 1 publication(s) receiving 21 citation(s).

Multiple viewpoints based overview for face recognition

Abstract: Face recognition has the wide research and applications on many areas. Many surveys of face recognition are implemented. Different from previous surveys on from a single viewpoint of application, method or condition, this paper has a comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions. In the sensory inputs, we review face recognition from image-based, video-based, 3D-based and hypersprectral image based face recognition, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented, such as kernel learning, manifold learning method. Moreover we discuss the single training sample based face recognition and under the variable poses. The prominent algorithms are described and critically analyzed, and relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed

An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

Abstract: With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA) to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filte...

A novel multiplex cascade classifier for pedestrian detection

Abstract: Reliable pedestrian detection is of great importance in visual surveillance In this paper, we propose a novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifier The Haar-like cascade classifier filters out most of irrelevant image background, while the shapelet cascade classifier detects intensively head-shoulder features The weighted linear regression model is introduced to train its weak classifiers We also introduce a structure table to label the foreground pixels by means of background differences The experimental results illustrate that our classifier model provides satisfying detection accuracy In particular, our detection approach can also perform well for low resolution and relatively complicated backgrounds

Noise-free representation based classification and face recognition experiments

Abstract: The representation based classification has achieved promising performance in high-dimensional pattern classification problems. As we know, in real-world applications the samples are usually corrupted by noise. However, representation based classification can take only noise in the test sample into account and is not able to deal with noise in the training sample, which causes side-effect on the classification result. In order to make the representation based classification more suitable for real-world applications such as face recognition, we propose a new representation based classification method in this paper. This method can effectively and simultaneously reduce noise in the test and training samples. Moreover, the proposed method can reduce noise in both the original and virtual training samples and then exploits them to determine the label of the test sample. The virtual training sample is generated from the original face image and shows possible variation of the face in scale, facial pose and expression. The experimental results show that the proposed method performs very well in face recognition.

Semi-supervised Facial Expression Recognition Algorithm on The Condition of Multi-pose

Abstract: A major challenge in pattern recognition is labeling of large numbers of sam- ples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression recognition based on semi- supervised learning. However, pose-variants caused by body movement, may decrease facial expression recognition rate. A novel facial expression recognition algorithm based on semi-supervised learning is proposed to improve the robustness in multi-pose facial expression recognition. In the proposed method, transfer learning has been brought into semi-supervised learning to solve the problem of multi-pose facial expression recognition. Experiments show that our method is competent for semi-supervised facial expression recognition on the condition of multi-pose. The recognition rates are 82.68% and 87.71% on the RaFD database and BHU database, respectively.