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Khoa học giáo dục học nói chung, bao gồm cả đào tạo, sư phạm học, lý luận giáo dục,..

Phùng Thái Thiên Trang, Fukuzawa Masayuki, Lý Quốc Ngọc(1)

Tổng quan về phương pháp học thuộc tính mặt người

An overview of facial attribute learning

Tạp chí Khoa học - Đại học Sư phạm TP Hồ Chí Minh

2021

3

572-591

1859-3100

Học sâu, Học thuộc tính mặt người, Học đa nhiệm, Sự mất cân bằng dữ liệu, Phả hệ tri thức

Deep learning, Facial attribute learning, Facial attribute ontology, Imbalanced data solver, Multi-task learning

Thuộc tính mặt người là thông tin hữu ích cho việc xây dựng các ứng dụng như nhận dạng, tìm kiếm và giám sát khuôn mặt người. Do đó, chúng rất quan trọng đối với các nhiệm vụ phân tích khuôn mặt khác nhau. Nhiều thuật toán học thuộc tính khuôn mặt người đã và đang được phát triển để tự động phát hiện các thuộc tính trong nhiều năm qua. Trong bài báo này, chúng tôi khảo sát một số phương pháp điển hình về học thuộc tính khuôn mặt người. Chúng tôi chia ra năm loại chính của các phương pháp: (1) Học truyền thống, (2) Học sâu đơn nhiệm, (3) Học sâu đa nhiệm, (4) Giải quyết vấn đề mất cân bằng dữ liệu và (5) Thuộc tính khuôn mặt dựa vào phả hệ tri thức. Các phương pháp bao gồm từ học truyền thống đến học sâu, cùng với các phương pháp hỗ trợ giải quyết bài toán lỗ hổng ngữ nghĩa dựa trên phả hệ tri thức và giải quyết sự mất cân bằng dữ liệu. Đối với mỗi phương pháp trong mỗi loại, chúng tôi thảo luận về các lí thuyết cơ bản cũng như điểm mạnh, điểm yếu và sự khác biệt của chúng. Chúng tôi cũng so sánh hiệu suất của chúng trên bộ dữ liệu tiêu chuẩn. Cuối cùng, dựa trên đặc điểm và đóng góp của các phương pháp, chúng tôi đưa ra kết luận và hướng nghiên cứu trong tương lai để giải quyết vấn đề học thuộc tính khuôn mặt. bài khảo sát này sẽ giúp các nhà nghiên cứu có góc nhìn tổng quan nhanh để xây dựng các ứng dụng khuôn mặt người trong tương lai cũng như các nghiên cứu mới.

Facial attributes are useful for developing applications such as face recognition, search, and surveillance. They are therefore important for various facial analysis. Many facial attribute learning algorithms have been developed to automatically detect those key attributes over the years. In this paper, we have surveyed some typical facial attribute learning methods. Five major categories of the state-of-the-art methods are identified: (1) Traditional learning, (2) Deep Single Task Learning, (3) Deep Multitask Learning, (4) Imbalanced Data Solver, and (5) Facial Attribute Ontology. They included from traditional learning algorithm to deep learning, along with methods that assist in solving semantic gaps based on ontology and solving data imbalances. For each algorithm of category, basic theories as well as their strengths, weaknesses, and differences are discussed. We also compared their performance on the standard datasets. Finally, based on characteristics and contribution of methods, we present conclusion and future works to solve facial attributes learning. The survey can help researchers gain a quick overview to build future human face applications as well as further studies.

TTKHCNQG, CTv 138

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