Sistem Kontrol Akses Berbasis Real TIME Face Recognition Dan Gender Information

Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systemsand it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information inthis system based on the Principal Component Analysis method (PCA). Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In thisaccess control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA ). The result achievedfrom the application design is the identification of a person's face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy

Evaluating soft biometrics in the context of face recognition

2013 Summer.Includes bibliographical references.Soft biometrics typically refer to attributes of people such as their gender, the shape of their head, the color of their hair, etc. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. Here four experiments quantify performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using information associated with soft biometrics. These experiments include a best-case analysis using perfect knowledge of gender and race, support vector machine-based soft biometric classifiers, face shape expressed through an active shape model, and finally appearance information from the image region directly surrounding the face. All four experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. In the context of face recognition, empirical evidence suggests that significant gains using soft biometrics are hard to come by

Gender and Ethnicity Classification Using Partial Face in Biometric Applications

As the number of biometric applications increases, the use of non-ideal information such as images which are not strictly controlled, images taken covertly, or images where the main interest is partially occluded, also increases. Face images are a specific example of this. In these non-ideal instances, other information, such as gender and ethnicity, can be determined to narrow the search space and/or improve the recognition results. Some research exists for gender classification using partial-face images, but there is little research involving ethnic classifications on such images. Few datasets have had the ethnic diversity needed and sufficient subjects for each ethnicity to perform this evaluation. Research is also lacking on how gender and ethnicity classifications on partial face are impacted by age. If the extracted gender and ethnicity information is to be integrated into a larger system, some measure of the reliability of the extracted information is needed. This study will provide an analysis of gender and ethnicity classification on large datasets captured by non-researchers under day-to-day operations using texture, color, and shape features extracted from partial-face regions. This analysis will allow for a greater understanding of the limitations of various facial regions for gender and ethnicity classifications. These limitations will guide the integration of automatically extracted partial-face gender and ethnicity information with a biometric face application in order to improve recognition under non-ideal circumstances. Overall, the results from this work showed that reliable gender and ethnic classification can be achieved from partial face images. Different regions of the face hold varying amount of gender and ethnicity information. For machine classification, the upper face regions hold more ethnicity information while the lower face regions hold more gender information. All regions were impacted by age, but the eyes were impacted the most in texture and color. The shape of the nose changed more with respect to age than any of the other regions

The Role of Face Parts in Gender Recognition

This paper evaluates the discriminant capabilities of face parts in gender recognition. Given the image of a face, a number of subimages containing the eyes, nose, mouth, chin, right eye, internal face (eyes, nose, mouth, chin), external face (hair, ears, contour) and the full face are extracted and represented as appearance-based data vectors. A greater number of face parts from two databases of face images (instead of only one) were considered with respect to previous related works, along with several classification rules. Experiments proved that single face parts offer enough information to allow discrimination between genders with recognition rates that can reach 86%, while classifiers based on the joint contribution of internal parts can achieve rates above 90%. The best result using the full face was similar to those reported in general papers of gender recognition (>95%). A high degree of correlation was found among classifiers as regards their capacity to measure the relevance of face parts, but results were strongly dependent on the composition of the database. Finally, an evaluation of the complementarity between discriminant information from pairs of face parts reveals a high potential to define effective combinations of classifiers

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International Conference on Pattern Recognition (ICPR2018

Spontaneous Gender Categorization in Masking and Priming Studies: Key for Distinguishing Jane from John Doe but Not Madonna from Sinatra

Facial recognition is key to social interaction, however with unfamiliar faces only generic information, in the form of facial stereotypes such as gender and age is available. Therefore is generic information more prominent in unfamiliar versus familiar face processing? In order to address the question we tapped into two relatively disparate stages of face processing. At the early stages of encoding, we employed perceptual masking to reveal that only perception of unfamiliar face targets is affected by the gender of the facial masks. At the semantic end; using a priming paradigm, we found that while to-be-ignored unfamiliar faces prime lexical decisions to gender congruent stereotypic words, familiar faces do not. Our findings indicate that gender is a more salient dimension in unfamiliar relative to familiar face processing, both in early perceptual stages as well as later semantic stages of person construal