Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.