  Human identification is a much attention problem in computer vision. Gender
classification plays an important role in human identification as preprocess
step. So far, various methods have been proposed to solve this problem.
Absolutely, classification accuracy is the main challenge for researchers in
gender classification. But, some challenges such as rotation, gray scale
variations, pose, illumination changes may be occurred in smart phone image
capturing. In this respect, a multi step approach is proposed in this paper to
classify genders in human face images based on improved local binary patters
(MLBP). LBP is a texture descriptor, which extract local contrast and local
spatial structure information. Some issues such as noise sensitivity, rotation
sensitivity and low discriminative features can be considered as disadvantages
of the basic LBP. MLBP handle disadvantages using a new theory to categorize
extracted binary patterns of basic LBP. The proposed approach includes two
stages. First of all, a feature vector is extracted for human face images based
on MLBP. Next, non linear classifiers can be used to classify gender. In this
paper nearest neighborhood classifier is evaluated based on Tani-Moto metric as
distance measure. In the result part, two databases, self-collected and ICPR
are used as human face database. Results are compared by some state-ofthe-art
algorithms in this literature that shows the high quality of the proposed
approach in terms of accuracy rate. Some of other main advantages of the
proposed approach are rotation invariant, low noise sensitivity, size invariant
and low computational complexity. The proposed approach decreases the
computational complexity of smartphone applications because of reducing the
number of database comparisons. It can also improve performance of the
synchronous applications in the smarphones because of memory and CPU usage
reduction.
