This study refers mainly to the characteristics of symmetry and texture features in order to correctly locate a face within an image. Since we target facial expression and illumination variation in a facial image, this first requires an equalization process of adaptive smoothing of the shadows of the face caused by varying illumination. Following this, for symmetry axis detection, the study will address: Gradient Detection, Image Width and Location of Symmetry Axes, Symmetry Axes for Gradient Histogram (SAGH) and Selection; Weight is also added to strengthen symmetry characteristics. In order to verify the accuracy of the method, this study will use 6 experimental methods, namely SAGH, SAPG, WSAGH, WSAPG, WSAGH for no adaptive smoothing, and WSAPG for no adaptive smoothing. The image database used for this experiment is the Yale Face Database, with facial images that are subjected to different illumination, masked by shelters and displaying varying facial expressions. The experiment results show that the WSAPG method is the most accurate; achieving a 96.36% LM value, with the lowest GM value; it was the most successful at locating a face in the image. Hopefully it will be applied to enhancing current face recognition technology.