Face recognition with age
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Face recognition techniques have traditionally been used to authenticate individuals based on their facial features. However, aging poses a challenge as faces change over time. Recent approaches to age-invariant face recognition can be divided into local and holistic categories. Local approaches analyze distinctive features like skin texture and SIFT features, while holistic models try to generate aging functions to simulate facial changes. Larger databases are needed to train models on the aging process and improve age-invariant recognition.
![Database Available
Among many available face databases around
the world [14],
three of them includes significant sets for
aging individuals.
• MORPH Database
• FG-NET
• FERET Database](https://image.slidesharecdn.com/facerecognitionwithage-150215020850-conversion-gate01/85/Face-recognition-with-age-14-320.jpg)
Face recognition with age
- 1. Face recognition with age
- 2. follow me on Facebook.com/prabhatksuman Of RCC INSTITUTE OF INFORMATION TECHNOLOGY Canal South Road, Beliaghata, Kolkata, West Bengal 700015
- 4. Introduction If we ignore some early implementations, by some medieval Chinese merchants who had used fingerprints to settle serious business transactions, or some forgetful Chinese parents using fingerprints and footprints to differentiate children from one another, for hundreds of thousands years, face recognition had always been the only tool for mankind for authentication and authorization purpose.
- 5. Face images displaying aging variation. Each row shows images of the same individual
- 6. Application To catch terrorist !! To verify photograph on passport. Computer system authentification.
- 7. Motivation Although holistic model based approaches have been applied efficiently in age invariant face recognition they still have some drawbacks. The available face aging databases are usually collected from scanned images in different poses, illumination and expression. Meanwhile most face modeling methods require having face images with frontal pose, normal illumination and neutral expression to get the best fit results. Systems have to use a huge number of training images that are usually inefficient for the currently limited face aging databases
- 8. Earlier Work One of the first papers related to face aging from digital images belongs to Kwon. Their approach was based on geometric ratios of key face features and wrinkle analysis. Generally, recent proposed age related face recognition methods can be divided into two categories: local approaches and holistic approaches.
- 10. Biometric Recognition Measures uniqueness of skin texture It is called surface texture analysis. Picture taken called the skin print and broken into small block. This blocks are converted to mathematical code using algorithm. Helps to identify twins.
- 11. 3D image recognition 3 D image are more accurate than 2-D images. Subject do not have to see directly into camera. Change in amount of ambient light do not reduce effectiveness.
- 12. Global Approach Most holistic approaches try to generate face aging models and build aging functions to simulate or compensate for the aging process. Genetic Algorithms to optimize the aging function. Geng introduced an AGing pattErn Subspace (AGES) on the assumption that similar faces age in similar ways for all individuals.
- 13. Local Approaches Li introduced an advanced algorithm for face recognition against age invariant using multi-feature discriminant analysis (MFDA), which combines scale invariant feature transform (SIFT) and multi-scale local binary patterns (MLBP), to encode the local features. Fu also proposed a manifold learning technique in which a low dimensional manifold is learn from a set of age separated face images.
- 14. Database Available Among many available face databases around the world [14], three of them includes significant sets for aging individuals. • MORPH Database • FG-NET • FERET Database