Reconstructed Error and Linear Representation Coefficients Restricted by l1‐Minimization for Face Recognition under Different Illumination and Occlusion

Wu, Xuegang; Fang, Bin; Tang, Yuan Yan; Zeng, Xiaoping; Chen, Xing

doi:10.1155/2017/1458412

Cited by 4 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…it's a measure of matching, the bigger the item is, ∬ ( ! − " ) " # the smaller, the lighter the mismatch degree is, the better the matching degree is [26][27].…”

Section: ) Cross Correlation Matchingmentioning

confidence: 99%

Retracted: Image Recognition Technology Based on Neural Network

Chen

2020

IEEE Access

View full text Add to dashboard Cite

Facial expression recognition is an important part of human-computer interaction. Using deep learning algorithms to recognize and classify facial expressions has become a hot issue for scholars from all walks of life. In this paper, the traditional classification algorithm based on the convolutional neural network is improved, and the feature information of the key parts of the face is used to integrate the key part features with the global features of the face image to better distinguish similar categories. Therefore, this paper designs a method to locate the key points of the face image and optimizes the key point positioning method through multiple experiments to facilitate the extraction of the feature information of the key points. For the calculation of classification results, a multi-region test method is used. By calculating multiple regions of the image during the test, the accuracy of image recognition can be improved. The final experimental results show that the model with key point feature information has more advantages in accuracy, and the robustness of the model is improved.

show abstract

“…it's a measure of matching, the bigger the item is, ∬ ( ! − " ) " # the smaller, the lighter the mismatch degree is, the better the matching degree is [26][27].…”

Section: ) Cross Correlation Matchingmentioning

confidence: 99%

Retracted: Image Recognition Technology Based on Neural Network

Chen

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It has a wide range of practical engineering applications, including access control, video surveillance, and human computer interaction. Many works have been developed towards robust face recognition systems [1][2][3][4], and they usually encounter several challenging issues, such as occlusions, illumination changes, and pose variations [1]. Among these issues, face recognition under large head pose variations is one of the most challenging problems [5][6][7][8][9][10], mainly due to the fact that the face appearance dramatically changes under different head poses.…”

Section: Introductionmentioning

confidence: 99%

Directional Correlation Filter Bank for Robust Head Pose Estimation and Face Recognition

Chen

Yan

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

During the past few decades, face recognition has been an active research area in pattern recognition and computer vision due to its wide range of applications. However, one of the most challenging problems encountered by face recognition is the difficulty of handling large head pose variations. Therefore, the efficient and effective head pose estimation is a critical step of face recognition. In this paper, a novel feature extraction framework, called Directional Correlation Filter Bank (DCFB), is presented for head pose estimation. Specifically, in the proposed framework, the 1-Dimensional Optimal Tradeoff Filters (1D-OTF) corresponding to different head poses are simultaneously and jointly designed in the low-dimensional linear subspace. Different from the traditional methods that heavily rely on the precise localization of the key facial feature points, our proposed framework exploits the frequency domain of the face images, which effectively captures the high-order statistics of faces. As a result, the obtained features are compact and discriminative. Experimental results on public face databases with large head pose variations show the superior performance obtained by the proposed framework on the tasks of both head pose estimation and face recognition.

show abstract