Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models
| Autor: | Norah Alkharashi, Nourah Aloboud, Heba Kurdi, Mead Alrshoud, Alhanoof Althnian, Faten Alduwaish |
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| Rok vydání: | 2020 |
| Předmět: |
gender recognition
Computer science SVM Feature extraction 0211 other engineering and technologies 02 engineering and technology lcsh:Technology Convolutional neural network lcsh:Chemistry 0202 electrical engineering electronic engineering information engineering feature fusion hand-crafted features General Materials Science lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes 021110 strategic defence & security studies lcsh:T business.industry Process Chemistry and Technology Deep learning General Engineering deep learning Pattern recognition Learning models deep-learned features lcsh:QC1-999 Computer Science Applications Support vector machine ComputingMethodologies_PATTERNRECOGNITION lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Face (geometry) Performance comparison Related research 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business lcsh:Physics CNN |
| Zdroj: | Applied Sciences Volume 11 Issue 1 Applied Sciences, Vol 11, Iss 89, p 89 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11010089 |
| Popis: | Face gender recognition has many useful applications in human&ndash robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05. |
| Databáze: | OpenAIRE |
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