Project ProposalAge Estimation using face image

D10303810

Imtiyaz

IntroductionThe appearance of human faces change by aging and at different age segment we can find the different aging patterns. The main changes are bone movement and growth, skin wrinkles and muscle strength reduction.

These features can be used in an algorithm which enable the estimation of a person’s age; based on features derived from his/her face image.

Applicatons1.Age based access control:-age related entrance restriction, preventing purchase of certain goods

2. Age Adaptive Human Machine Interaction (HCI):To determine the age of a computer/machine user and automatically adjust the user interface in order to suit the needs of his/her age group. e.g- can be used in public information kiosk3. Age Invariant Person Identification:can be developed by applying age progression techniques for deforming the face of a subject in order to predict how the subject will look like in the future.4. Data mining and organization: age-based retrieval and classification of face images can be used in automatically sorting and image retrieval from e-photo albums and the internet.

Methodology

Gabor filterPCA

Removing tiltResize

Steps: Age feature extraction: The age feature extractor is

constructed using Gabor wavelets that will used for image analysis.

Feature reduction: DCT will follow with Gabor wavelet feature extraction to reduce dimensionality of the transformed data• Feature classification: The Gabor wavelet features are used in

the SVM classifier to indentify how old the face is

Gabor filter

A set of Gabor filters with different frequencies and orientations is used for extracting useful features from an image

creates a u*v array, whose elements are mxn matrices; each matrix being a 2-D Gabor filter. extracts the Gabor features of the image gives column vector, consisting of the image's Gabor features.

Discrete cosine transformationThe discrete cosine transform (DCT) helps separate the image into parts (or spectral sub-bands) of differing importance.It transforms a signal or image from the spatial domain to the frequency domain

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Support Vector MachinesA Support Vector Machine (SVM) is a supervised learning method, which uses so called support vectors to build a model for classification or regression. The aim is to find an optimal hyperplane to separate two classes

Let P be linear separable two class problem

One m dimensional vector of training set D

To maximize margin between classes h1 and h2,

Parameters w and b are scaled such that for each pt.

To maximize the distance between h1 and h2 i.e , is minimized

Database:-The FG‐NET Aging Database will be used to investigate the performance of age estimation system. The database contains 1,002 high‐resolution colour and grey‐scale face images with large variations in lighting, pose and expression. There are a total of 82 subjects (multiple races) ranged in age from 0 to 69 years.

LOPO

For each classifier, Leave-One-Person-Out (LOPO)evaluation scheme is used. In each fold, all samples of a single person are used as the testing set and the remaining samples are used as the training set.

References:-1. Chin-Teng Lin, Dong-Lin Li, Jian-Hao Lai, Ming-Feng Han and Jyh-Yeong Chang:

2. Mohamed Y. El Dib and Bassam S. Abou Zaid, “Human age estimation using extended Gabor like-features”

3. “Automatic Age Estimation System for Face Images”, Int J Adv Robotic Sy, 2012, Vol. 9, 216:2012

4. C. Arun Kumar1, Praveen Kumar R V, Sai Arvind R, “Age Group Estimation using Facial Features”IJETCAS 14-351; 2014

5. Sangeeta Agrawal1, Rohit Raja2, Sonu Agrawal3, “Support Vector Machine for age classification” International Journal of Emerging Technology and Advanced Engineering(ISSN 2250-2459, Volume 2, Issue 5, May 2012)

6. Rishi Gupta, Anuj Khunteta, “SVM Age Classify based on the facial images” IJCCN, Volume 1, No.2, September – October 2012

7. Fatma Guney, “Gender and Age Estimation Using Face Images”

8. http://www.scholarpedia.org/article/Facial_Age_Estimation

9. The FG‐NET Aging Database [Online]. Available:http://www.fgnet.rsunit.com/

10. Chih-Jen Lin Chih-Chung Chang. Libsvm: A library for support vector machines,2001.

11. http://blog.csdn.net/guoming0000/article/details/7839917