In this paper, we present face recognition systems, which are performed by using appearance based methods on mobile environment face images, and their comparative performance analysis. In proposed systems, face detection process is performed by using Haar-like features and cascade classifiers on mobile environment face images. Color space transformation, dimensional normalization and histogram equalization operations are performed on detected face images as pre-processing steps. Principal Component Analysis, Fisher's Linear Discriminant Analysis and Local Binary Pattern Histograms methods are used to extract facial features. K-nearest neighbor classifier is employed for the performance analysis of implemented methods. Accuracy, precision, recall and F-measure values are measured and compared in performance evaluations of selected facial recognition methods on various dimensionally normalized face images. Experimental results obtained using MOBIO face database show that Local Binary Pattern Histograms method has high success rates on mobile environment images.