Age Classification from Face Images Focusing on Edge Information

Nakano, Miyoko; Yasukata, Fumiko; Fukumi, Minoru

doi:10.1007/978-3-540-30132-5_121

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…One of the proposed models to solve this problem is called Age Classification, which detects the age of a person in photo based on his face (Kwon & da Vitoria Lobo, 1994;Nakano, Yasukata & Fukumi, 2004;Lanitis, Draganova & Christodoulou, 2004;Horng, Lee & Chen 2001). Another approach is to devise a model extracting the features concerning the age from the sample of face images containing already known age, and then reconstructing face images of each person for each age group.…”

Section: Background and Motivationmentioning

confidence: 99%

Cross Models for Twin Recognition

Nguyen

Wan

2016

International Journal of Digital Crime and Forensics

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Nowadays, biometrics has become a popular tool in personal identification as it utilizes physiological or behavioral characteristics to identify individuals. Recent advancement in computer science has increased the accuracy of biometrics to a higher level. However, there are still a number of existing problems, such as complex environment, aging and unique problems. Twin identification is notably one of the most challenging issues, because they resemble each other in terms of biometrics. The similarity affects the use of biometrics in general cases and raised the potential risk of biometrics in access control.This thesis presents and compares four methods for twin recognition, namely, ear recognition, speaker recognition and lip movement recognition. Our results show that speaker recognition has the best performance with 100% accuracy. This is much higher than that of face recognition and ear recognition (with 58% and 53% respectively) while movement recognition that yields 76% accuracy.The objectives of this thesis are to investigate whether it is possible to identify twins between each other by using biometrics and find out which recognition approach is the best one. Comparing to the-state-of-the-art technologies, our work has taken a further step in twin recognition by using biometrics.In future, we will take behavioral analysis of twins into consideration, since their growing-up environment may have impact on their behaviors. We assume that other clues for twin recognition could be discovered from the behavioral analysis.

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Section: Background and Motivationmentioning

confidence: 99%

Cross Models for Twin Recognition

Nguyen

Wan

2016

International Journal of Digital Crime and Forensics

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Automatic Facial Age Estimate Based on Convolution Neural Network

Zou

Yang

Zhang

et al. 2017

Advances in Intelligent Information Hiding and Multimedia Signal Processing

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Gender Recognition from Face Images with Dyadic Wavelet Transform and Local Binary Pattern

Ullah

Hussain

Aboalsamh

et al. 2012

Lecture Notes in Computer Science

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In various biometric applications, gender recognition from facial images plays an important role. In this paper, we investigate Weber's Local Descriptor (WLD) for gender recognition. WLD is a texture descriptor that performs better than other similar descriptors but it is holistic due to its very construction. We extend it by introducing local spatial information; divide an image into a number of blocks, calculate WLD descriptor for each block and concatenate them. This spatial WLD descriptor has better discriminatory power. Spatial WLD descriptor has three parameters. Through a large number of experiments performed on FERET database, we report the best combination of these parameters and that our proposed spatial WLD descriptor with simplest classifier gives much better accuracy i.e. 99.08% with lesser algorithmic complexity than state-of-the-art gender recognition approaches.

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Age Classification from Face Images Focusing on Edge Information

Cited by 5 publications

References 4 publications

Cross Models for Twin Recognition

Cross Models for Twin Recognition

Automatic Facial Age Estimate Based on Convolution Neural Network

Gender Recognition from Face Images with Dyadic Wavelet Transform and Local Binary Pattern

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