There are various types of biometric identifications including DNA, fingerprints, signature recognition, hand geometry, palm print, for this type of recognition; some action has to be done by the user like placing a finger on the machine to detect. While, Face Recognition does not require any user actions .It plays a vital role in fields such as identifying the retail crimes, finding missing person, to help the blind, forensic, to diagnose s, unlocks smart phones, secure transactions, validates identity, control access to sensitive areas etc. The challenge that can be encountered in face recognition is to detect the face from single image that is stored in the database. Face detection is a challenging task as the faces are not rigid and they will be changing in size, shape, colour, it become more challenging task when given image is not clear and not containing a proper lightning, not facing camera etc. The digital image can be obtained from a video frame or from live images detected by the camera/webcam. It involves two stages. First we have to detect the face. For this process, a photo is searched to find face, after finding the face in the image, which is processed to crop and extract the person's face in square box, the second phase is Face Recognition, where the face detected by above process is compared with the images in the dataset, to decide who that person is. In this work, we are using the HOG algorithm. A Face can be detected by cropping the face and removing the noise using the HAAR cascade classifier. Later the features of the face can be extracted by using the HOG extractor. Then we will run an SVM model for face recognition. The result is an evident for accuracy and efficiency.