Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
Articles 1 - 6 of 6
Full-Text Articles in Engineering
Facial Expression Recognition With Independent Subspace Analysis Based Feature Learning, Yongjie Zhan, Long Fei, Yikun Bu
Facial Expression Recognition With Independent Subspace Analysis Based Feature Learning, Yongjie Zhan, Long Fei, Yikun Bu
Journal of System Simulation
Abstract: Hand-designed features (such as Gabor, LBP) has been widely employed in facial expression recognition. In the real-world applications of facial expression recognition, it is very difficult to achieve perfect face alignment because of the impact of complex background and the limitations of face alignment approaches. Independent Subspace Analysis (ISA) is an unsupervised feature learning method, which can be used to learn phase-invariant visual features from images. The problem of facial expression recognition based on ISA in the situation of not precise face alignment was investigated. Through analyzing the facial expression recognition performances with different subspace size, it was turned …
A Computational Study On Aging Effect For Facial Expression Recognition, Elena Sönmez
A Computational Study On Aging Effect For Facial Expression Recognition, Elena Sönmez
Turkish Journal of Electrical Engineering and Computer Sciences
This work uses newly introduced variations of the sparse representation-based classifier (SRC) to challenge the issue of automatic facial expression recognition (FER) with faces belonging to a wide span of ages. Since facial expression is one of the most powerful and immediate ways to disclose individuals? emotions and intentions, the study of emotional traits is an active research topic both in psychology and in engineering fields. To date, automatic FER systems work well with frontal and clean faces, but disturbance factors can dramatically decrease their performance. Aging is a critical disruption element, which is present in any real-world situation and …
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Turkish Journal of Electrical Engineering and Computer Sciences
Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally …
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores a novel method to represent face images for facial expression recognition; it is named compact local Gabor directional number pattern (CLGDNP). By convolving the face images with Gabor filters, we encode the magnitude and phase response images in each scale, and calculate the histograms in several nonoverlapping regions of each encoded image. Finally, we obtain two spatial histogram sequences by the aid of the mean pooling technology and concatenate them to form the facial descriptor. Moreover, for evaluating the performance of the proposed method, we employ a support vector machine to conduct some extensive classification experiments on …
Adaptive Joint Block-Weighted Collaborative Representation For Facial Expression Recognition, Zhe Sun, Zhengping Hu, Meng Wang, Shuhuan Zhao
Adaptive Joint Block-Weighted Collaborative Representation For Facial Expression Recognition, Zhe Sun, Zhengping Hu, Meng Wang, Shuhuan Zhao
Turkish Journal of Electrical Engineering and Computer Sciences
Facial expression recognition (FER) plays a significant role in human-computer interactions. Recently, regularized linear representation-based classification has achieved satisfying results in FER. Considering that different blocks in a sample should contribute differently to the representation and classification, we propose an adaptive joint block-weighted collaborative representation-based classification (JBW_CRC) method to effectively exploit the similarity and distinctiveness of different blocks. In JBW_CRC, samples are divided into different blocks and each block of the query sample is represented as a feature vector. Each feature vector is coded on its related block dictionary, which considers the similarity among the feature vectors. Additionally, the distinctiveness …
A Facial Component-Based System For Emotion Classification, Elena Sönmez, Songül Albayrak
A Facial Component-Based System For Emotion Classification, Elena Sönmez, Songül Albayrak
Turkish Journal of Electrical Engineering and Computer Sciences
Smart environments with ubiquitous computers are the next generation of information technology, which requires improved human--computer interfaces. That is, the computer of the future must be aware of the people in its environment; it must know their identities and must understand their moods. Despite the great effort made in the past decades, the development of a system capable of automatic facial emotion recognition is still rather difficult. In this paper, we challenge the benchmark algorithm on emotion classification of the Extended Cohn-Kanade (CK$+)$ database, and we present a facial component-based system for emotion classification, which beats the given benchmark performance: …