  The growth in electronic transactions and human machine interactions rely on
the information such as gender, age, expression and ethnicity provided by the
face image. In order to obtain these information, feature extraction plays a
major role. In this paper, retrieval of age, gender, expression and race
information from an individual face image is analysed using different feature
extraction methods. The performance of four major feature extraction methods
such as Active Appearance Model (AAM), Gabor wavelets, Local Binary Pattern
(LBP) and Wavelet Decomposition (WD) are analyzed for gender recognition, age
estimation, expression recognition and racial recognition in terms of accuracy
(recognition rate), time for feature extraction, neural training and time to
test an image. Each of this recognition system is compared with four feature
extractors on same dataset (training and validation set) to get a better
understanding in its performance. Experiments carried out on FG-NET,
Cohn-Kanade, PAL face database shows that each method has its own merits and
demerits. Hence it is practically impossible to define a method which is best
at all circumstances with less computational complexity. Further, a detailed
comparison of age estimation and age estimation using gender information is
provided along with a solution to overcome aging effect in case of gender
recognition. An attempt has been made in obtaining all (i.e. gender, age range,
expression and ethnicity) information from a test image in a single go.
