Ecrime Face Movement Detection Using Template Matching

Ecrime Face Movement Detection Using Template Matching

Face recognition process can be used for individual verification and identification. Generating an image that can be used for identification, manipulation, modeling, pattern recognition, and object search is the main thing on face area determination. The template matching method used the intercourse between the input image pattern and the referral face pattern along with its features. In this paper will be purposed about face detection use template matching method on movement face. The technique used is to determine the face region by separating the skin region to non-skin region. Detected face area is dynamic. Faces can move horizontal or vertical. Then the results of the process is a face image model. The face image model will show whether the skin is a face region, which will also produce coordinates of the face region.  Ecrime Face Movement Detection Using Template Matching

Humans often use face detection to recognize other individuals. Initially, face recognition algorithm used simple geometric model, then this model continues to better evolve so that it becomes the representation of advanced mathematics and matching processes. The last ten to fifteen years, great advances and initiatives have encouraged face recognition technology into an alternative method. Face
recognition process can be used for individual verification and identification. In another case, partial face recognition development has generated a lot of literature, but only part of the study has tried to analyze whether and how the partial face biometry actually appears to have the negative effect on the
level of accuracy and error

Identify the target face on an observation, where the face is placed regardless of position, scale, orientation, lighting conditions, expression, etc. Faced with these challenges, previous face detection research had focused on computer vision [4]. Generating an image that can be used for
identification, manipulation, modeling, pattern recognition, and object search is the main thing on face area determination

In this paper will be discussed the face detection use template matching method on moving face. The technique used is to determine the face region by separating the skin region to non-skin region. Detected face area is dynamic. Faces can move horizontal or vertical. Then the results of the process is a face image model. The face image model will show whether the skin is a face region, which will also
produce coordinates of the face region

The face image is an important analysis in the interaction between human-computer (Human-Computer Interaction / HCI) based on computer vision study. Face detection is a series of process to find solutions where the position of the image should be determined. The goal is to identify all areas of the image containing a face. In the case, the process must not ignore the positioning factor of three-dimensional,
direction, and lighting conditions. Display images such as pose, scale, rotation and image orientation, face expressions, are difficulties related to face detection systems due to variations in images. Face detection is when fluctuating image is given, face detection will determine whether there is a face or not in the image, and if there is a face, the location and extent of the image will be determined. The things that effect in face detection are; position, the presence of structural components, face expression, occlusion, image direction, and image condition. Skin color has been used and proven to be an effective feature used in face detection. Although each human being has different skin color, the main difference is in the intensity of the color.

Any results obtained from the camera will be displayed in intact. Both from the side of the face as well all the color attributes of the features that come with it. The resolution used in capturing the image is 320 x 240 pixels. Rotating position is the process of taking face image in several positions, which is illustrated in figure 9. In the picture, the face image rotates in two directions, That is clockwise and counter clockwise