Automated human age estimation based on face images

Human age as an important personal trait can be applied in a variety of settings such as biometric airport security checks or access to product such as alcohol or tobacco in a shop. However, can computers perform age recognition function like what human being did? In this project, the author had developed an age estimation based on facial images system through wrinkles emerging from the facial appearance due to biologic aging. These facial images can either be extract from live webcam or existing digital photo images. The author has made use of local Successive Mean Quantization Transforms (SMQT) to extract feature for face detection follow by Sparse Network of Winnows (SNOW) classifier for face prediction. Upon the detection of face, the image is crop and thereafter spatially localized spectral features will be extracted using Gabor filter. Subsequently, these extracted spectral features are transformed into corresponding Eigen faces using Principle Component Analysis (PCA). This technique allows dimension reduction output in high compression rate for faster estimation. Lastly, results are classify into 4 age groups consisting of “Child”, “Teen”, “Adult” and “Senior adult” with Extreme Learning Machine (ELM) classifier that perform good generalization performance at tremendously fast learning rate.Bachelor of Engineerin