Result Oriented Based Face Recognition using Neural Network with Erosion and Dilation Technique

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery and wearing the spec/glasses which are the new challenge to automatic face recognition. Face detection is one of the challenging problems in the image processing. This seminar, introduce a face detection and recognition system to detect (finds) faces from database of known people. To detect the face before trying to recognize it saves a lot of work, as only a restricted region of the image is analyzed, opposite to many algorithms which work considering the whole image. In This , we gives study on Face Recognition After Plastic Surgery (FRAPS )and after wearing the spec/glasses with careful analysis of the effects on face appearance and its challenges to face recognition. To address FRAPS and wearing the spec/glasses problem, an ensemble of An Optimize Wait Selection By Genetic Algorithm For Training Artificial Neural Network Based On Image Erosion and Dilution Technology. Furthermore, with our impressive results, we suggest that face detection should be paid more attend to. To address this problem, we also used Edge detection method to detect i/p image properly or effectively. With this Edge Detection also used genetic algorithm to optimize weight using artificial neural network (ANN)and save that ANN file to database .And use that ANN file to compare face recognition in future DOI: 10.17762/ijritcc2321-8169.16041

Computer Aided Morphological Analysis for maxillo-facial diagnostic: a preliminary study

This article compares most of the three-dimensional (3D) morphometric methods currently proposed by the technical literature to evaluate their morphological informative value, while applying them to a case study of five patients affected by the malocclusion pathology. The compared methods are: conventional cephalometric analysis (CCA), generalised Procrustes superimposition (GPS) with principal-components analysis (PCA), thin-plate spline analysis (TPS), multisectional spline (MS) and clearance vector mapping (CVM). The results show that MS provides more reliable and useful diagnostic informatio

Video-based assistance system for training in minimally invasive surgery

In this paper, the development of an assisting system for laparoscopic surgical training is presented. With this system, we expect to facilitate the training process at the first stages of training in laparoscopic surgery and to contribute to an objective evaluation of surgical skills. To achieve this, we propose the insertion of multimedia contents and outlines of work adapted to the level of experience of trainees and the detection of the movements of the laparoscopic instrument into the monitored image. A module to track the instrument is implemented focusing on the tip of the laparoscopic tool. This tracking method does not need the presence of artificial marks or special colours to distinguish the instruments. Similarly, the system has another method based on visual tracking to localize support multimedia content in a stable position of the field of vision. Therefore, this position of the support content is adapted to the movements of the camera or the working area. Experimental results are presented to show the feasibility of the proposed system for assisting in laparoscopic surgical training

Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

Assessing facial beauty through proportion analysis by image processing and supervised learning

Perception of universal facial beauty has long been debated amongst psychologists and anthropologists. In this paper, we perform experiments to evaluate the extent of universal beauty by surveying a number of diverse human referees to grade a collection of female facial images. Results obtained show that there exists a strong central tendency in the human grades, thus exhibiting agreement on beauty assessment. We then trained an automated classifier using the average human grades as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry, cosmetic industry, virtual media, and plastic surgery. © 2006 Elsevier Ltd. All rights reserved