Recognizing age-separated face images: humans and machines.
Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4256302?pdf=render |
| _version_ | 1811236472304435200 |
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| author | Daksha Yadav Richa Singh Mayank Vatsa Afzel Noore |
| author_facet | Daksha Yadav Richa Singh Mayank Vatsa Afzel Noore |
| author_sort | Daksha Yadav |
| collection | DOAJ |
| description | Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. |
| first_indexed | 2024-04-12T12:10:26Z |
| format | Article |
| id | doaj.art-a02cedf0930045d5aa05ec2cb6f1a68f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T12:10:26Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a02cedf0930045d5aa05ec2cb6f1a68f2022-12-22T03:33:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11223410.1371/journal.pone.0112234Recognizing age-separated face images: humans and machines.Daksha YadavRicha SinghMayank VatsaAfzel NooreHumans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.http://europepmc.org/articles/PMC4256302?pdf=render |
| spellingShingle | Daksha Yadav Richa Singh Mayank Vatsa Afzel Noore Recognizing age-separated face images: humans and machines. PLoS ONE |
| title | Recognizing age-separated face images: humans and machines. |
| title_full | Recognizing age-separated face images: humans and machines. |
| title_fullStr | Recognizing age-separated face images: humans and machines. |
| title_full_unstemmed | Recognizing age-separated face images: humans and machines. |
| title_short | Recognizing age-separated face images: humans and machines. |
| title_sort | recognizing age separated face images humans and machines |
| url | http://europepmc.org/articles/PMC4256302?pdf=render |
| work_keys_str_mv | AT dakshayadav recognizingageseparatedfaceimageshumansandmachines AT richasingh recognizingageseparatedfaceimageshumansandmachines AT mayankvatsa recognizingageseparatedfaceimageshumansandmachines AT afzelnoore recognizingageseparatedfaceimageshumansandmachines |