Benchmarking deep network architectures for ethnicity recognition using a new large face dataset
| Autor: | Gennaro Percannella, Mario Vento, Vincenzo Vigilante, Antonio Greco |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
0211 other engineering and technologies 02 engineering and technology Benchmark computer.software_genre Convolutional neural network Field (computer science) Dataset Deep learning Ethnicity recognition Face analysis Soft biometrics 0202 electrical engineering electronic engineering information engineering 021110 strategic defence & security studies Network architecture business.industry Benchmarking Expression (mathematics) Computer Science Applications Hardware and Architecture Pattern recognition (psychology) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business computer Software Natural language processing |
| Zdroj: | Machine Vision and Applications. 31 |
| ISSN: | 1432-1769 0932-8092 |
| DOI: | 10.1007/s00138-020-01123-z |
| Popis: | Although in recent years we have witnessed an explosion of the scientific research in the recognition of facial soft biometrics such as gender, age and expression with deep neural networks, the recognition of ethnicity has not received the same attention from the scientific community. The growth of this field is hindered by two related factors: on the one hand, the absence of a dataset sufficiently large and representative does not allow an effective training of convolutional neural networks for the recognition of ethnicity; on the other hand, the collection of new ethnicity datasets is far from simple and must be carried out manually by humans trained to recognize the basic ethnicity groups using the somatic facial features. To fill this gap in the facial soft biometrics analysis, we propose the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset, composed by more than 3,000,000 face images annotated with 4 ethnicity categories, namely African American, East Asian, Caucasian Latin and Asian Indian. The final annotations are obtained with a protocol which requires the opinion of three people belonging to different ethnicities, in order to avoid the bias introduced by the well-known other race effect. In addition, we carry out a comprehensive performance analysis of popular deep network architectures, namely VGG-16, VGG-Face, ResNet-50 and MobileNet v2. Finally, we perform a cross-dataset evaluation to demonstrate that the deep network architectures trained with VMER generalize on different test sets better than the same models trained on the largest ethnicity dataset available so far. The ethnicity labels of the VMER dataset and the code used for the experiments are available upon request at https://mivia.unisa.it. |
| Databáze: | OpenAIRE |
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