In this paper, 2D photographs image divided into two parts; one part is front view (x, y) and side view (y, z). Necessary condition of this method is that position or coordinate of both images should be equal. We combine both images according to the coordinate then we will get 3D Models (x, y, z) but this 3D model is not accurate in size or shape. In defining other words, we will get 3D face model, refinement of 3D face through edit of point and smoothing process. Smoothing is performed to get the more realistic 3D face model for the person. We measure to compare the average time for modeling and compare the research result of our methods with different techniques, for this purpose we taken by two hypotheses (1) the average quality of our method will be higher than the 60% (2) it is faster compare to other in an average case (3) it is automated. First hypothesis is correct but the second tie up with other three methods and third found satisfactory.

Face recognition is an example of advanced object recognition. The process is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Today there exist many well known techniques to try to recognize a face. We present to the reader an investigation into individual strengths and weaknesses of the most common techniques including feature based methods, PCA based eigenfaces, LDA based fisherfaces, ICA, Gabor wavelet based methods, neural networks and hidden Markov models. Hybrid systems try to combine the strengths and suppress the weaknesses of the different techniques either in a parallel or serial manner. Today there exist many well known techniques to try to recognize a face. Experiments done with implementations of different methods have shown that they have individual strengths and weaknesses. Hybrid systems try to combine the strengths and suppress the weaknesses of the different techniques either in a parallel or serial manner. The paper is to evaluate the different techniques and consider different combinations of these. Here we compare or evaluate templates based and geometry based face recognition, also give the comprehensive survey based face recognition methods.

The technology of face recognition has become mature within these few years. System, using the face recognition, has become true in real life. In this paper, we will have a comparative study of three most recently methods for face recognition. One of the approach is eigenface, fisherfaces and other one is the elastic bunch graph matching. After the implementation of the above three methods, we learn the advantages and Disadvantages of each approach and the difficulties for the implementation.

Biometric systems have been researched intensively by many organization. It overcomes the conventional security systems by identify ―who you are‖. This paper discusses the current image based biometric systems. It first gives some information about why biometric is needed and what should people look for in biometric systems. Several popular image based biometric systems have been examined in this paper. The technique used in each system for data acquisitions, feature extraction and classifiers are briefly discussed. The biometric systems included are face, fingerprint, hand geometry, hand vein, iris, retina and signature; here also describe the statistical approaches of biometrics. The paper concludes by examining the benefits of multi-modal biometric systems, it is found that there is no one good biometric systems each have its advantages and disadvantages and the performance of each biometric system is summarized.

In this Case Study & report, a face detection method is presented. Face detection is the first step of face Recognition methods. Face detection is a difficult task in Pattern. There are different methods of face detection namely-Knowledge Based Face Detection Methods, Feature Based Face Detection Methods, Template Based Face Detection Methods and Appearnce Based Face Detection Methods. But here we divided basically in two methods for face detection (i) image based methods (ii) feature based methods. We have developed an intermediate system, using a boosting algorithm to train a classifier which is capable of processing images rapidly while having high detection rates. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face recognition, the original Adaboost which uses all given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for classification. The main idea in the building of the detector is a learning algorithm based on boosting: AdaBoost. AdaBoost is an aggressive learning algorithm which produces a strong classifier by choosing visual features in a family of simple classifiers and combining them linearly. The family of simple classifiers contains simple rectangular wavelets which are reminiscent of the Haar basis. Their simplicity and a new image representation called Integral Image allow a very quick computing of these Haarlike features. Then a structure in cascade is introduced in order to reject quickly the easy to classify background regions and focus on the harder to classify windows. For this, classifiers with an increasingly complexity are combined sequentially. This improves both, the detection speed and the detection efficiency. The detection of faces in input images is proceeded using a scanning window at different scales which permits to detect faces of every size without resampling the original image. On the other hand, the structure of the final classifier allows a realtime implementation of the detector. Due to some limitation of neural network based methods we adopt the Adaboost algorithm for face detection. Here we present some results on real world examples are presented. Our detector found good detection rates with frontal faces and the method can be easily adapted to other object detection tasks by changing the contents of the training dataset.

This paper proposes the automatic face recognition method based on the face representation with five major processing modules- Filters, Face Location, Feature Location, Normalization, and Face Recognition. This precisely reflects the geometric features of the specific subject. We test our proposed algorithm database, and experimental results, show the effectiveness and competitive performance of the proposed method.