Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection
Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appe...
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| Published in | IEEE transactions on image processing Vol. 25; no. 5; pp. 2146 - 2154 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.05.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1057-7149 1941-0042 1941-0042 |
| DOI | 10.1109/TIP.2016.2535284 |
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| Abstract | Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods. |
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| AbstractList | Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods. Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods.Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods. |
| Author | Dacheng Tao Xuelong Li Zhifeng Li Dihong Gong |
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| Snippet | Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one,... |
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| SubjectTerms | Aging Aging - physiology aging faces Algorithms Biometric Identification - methods Databases, Factual Encoding Face Face - diagnostic imaging Face recognition Facial feature descriptor Feature extraction Humans Image coding Image Processing, Computer-Assisted - methods Learning Machine Learning Matching Mathematical models Tasks Transaction processing Visual Visualization |
| Title | Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection |
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