Face recognition has become increasingly important due to heightened security unrest in the world today. Traditionally, two dimensional (2D) images are used for recognition. However, they are affected by pose, illumination and expression changes. In this paper, a new three dimensional (3D) face matching technique that is able to recognize faces at various angles is proposed. This technique consists of three main steps, which are face feature detection, face alignment and face matching. The face feature detection section consists of face segmentation, eye area and corners detection, mouth area detection and nose area and tip detection. These features are detected using a combination of 2D and 3D images. An improved face area detection method is proposed. Besides that, a new method to detect the eyes and mouth corners automatically using curvature values is proposed. Finally, to detect the nose tip, a method that calculates nose tip candidates and filters them out based on their location is proposed. The feature positions are then used to achieve uniform alignment for the unknown probe face and those already in the database. Finally, face matching, which consists of surface matching, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is performed to identify the unknown probe face. The proposed method uses PCA and LDA on 3D images, instead of 2D images. Only the face area between the nose and forehead is used for recognition. This proposed technique is able to reduce the effects of pose, illumination and expression changes, which are common problems of 2D face recognition techniques. This is a fully automatic technique that does not require any user intervention at any step of its process.