Distribution Cognisant Loss for Cross-Database Facial Age Estimation With Sensitivity Analysis
| Autor: | Josef Kittler, Ali Akbari, Zhen-Hua Feng, Muhammad Awais, Ammarah Farooq |
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| Rok vydání: | 2020 |
| Předmět: |
Protocol (science)
Measure (data warehouse) Database Databases Factual Computer science Applied Mathematics 02 engineering and technology Function (mathematics) computer.software_genre Distribution (mathematics) Computational Theory and Mathematics Artificial Intelligence Age estimation Face 0202 electrical engineering electronic engineering information engineering Probability distribution 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Sensitivity (control systems) computer Software Algorithms |
| Zdroj: | IEEE transactions on pattern analysis and machine intelligence. 44(4) |
| ISSN: | 1939-3539 |
| Popis: | Existing facial age estimation studies have mostly focused on intra-database protocols that assume training and test images are captured under similar conditions. This is rarely valid in practical applications, where we typically encounter training and test sets with different characteristics. In this article, we deal with such situations, namely subjective-exclusive cross-database age estimation. We formulate the age estimation problem as the distribution learning framework, where the age labels are encoded as a probability distribution. To improve the cross-database age estimation performance, we propose a new loss function which provides a more robust measure of the difference between ground-truth and predicted distributions. The desirable properties of the proposed loss function are theoretically analysed and compared with the state-of-the-art approaches. In addition, we compile a new balanced large-scale age estimation database. Last, we introduce a novel evaluation protocol, called subject-exclusive cross-database age estimation protocol, which provides meaningful information of a method in terms of the generalisation capability. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art age estimation methods under both intra-database and subject-exclusive cross-database evaluation protocols. In addition, in this article, we provide a comparative sensitivity analysis of various algorithms to identify trends and issues inherent to their performance. This analysis introduces some open problems to the community which might be considered when designing a robust age estimation system. |
| Databáze: | OpenAIRE |
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