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Document Type |
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Thesis |
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Document Title |
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Semantic Human Face Analysis for Multi-level Age Estimation التحليل الدلالي لوجه الإنسان لتقدير متعدد المستويات للعمر |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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Human face is one of the most widely used biometrics and mostly based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its remarkable influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network models were used in our conducted experiments to assess the potency and efficacy of proposed semantic features under diverse circumstances. The experimental results showed noticeable performance improvements, when fusing semantic-based features with traditional computer-vision features, surpassing the performance of traditional features alone. |
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Supervisor |
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Dr. Emad Sami Jaha |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1443 AH
2022 AD |
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Added Date |
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Thursday, January 19, 2023 |
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Researchers
| روان سليمان حويان | Howyan, Rawan Sulaiman | Researcher | Master | |
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