Gender Classification Based on Multiscale Facial Fusion Feature
For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generate...
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| Veröffentlicht in: | Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-6 |
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| container_title | Mathematical problems in engineering |
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| creator | Shao, Zhuhong Shang, Yuanyuan Ding, Hui Zhang, Chunyu Fu, Xiaoyan |
| description | For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques. |
| doi_str_mv | 10.1155/2018/1924151 |
| format | Article |
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Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2018/1924151</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Architectural engineering ; Artificial intelligence ; Classification ; Electrical engineering ; Engineering ; Feature extraction ; Fourier transforms ; Gender ; Image classification ; Neural networks ; Pattern recognition ; Performance enhancement ; State of the art ; Studies ; Support vector machines</subject><ispartof>Mathematical problems in engineering, 2018-01, Vol.2018 (2018), p.1-6</ispartof><rights>Copyright © 2018 Chunyu Zhang et al.</rights><rights>Copyright © 2018 Chunyu Zhang et al. 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| subjects | Architectural engineering Artificial intelligence Classification Electrical engineering Engineering Feature extraction Fourier transforms Gender Image classification Neural networks Pattern recognition Performance enhancement State of the art Studies Support vector machines |
| title | Gender Classification Based on Multiscale Facial Fusion Feature |
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