We present two novel methods for face verification. Our first method – “attribute ” classifiers – uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method – “simile ” classifiers – removes

Abstract—The process of face verification is guided by a pre-learned global threshold, which, however, is often inconsistent with class-specific optimal thresholds. It is, hence, beneficial to pursue a balance of the class-specific thresholds in the model-learning stage. In this paper, we present a

In this chapter, we discuss the research issues and state-of-the-art of face verification for access control. We start by analyzing a typical face verification system for access control, and then explore the dominant technologies in the field. We introduce most of the typical and popular

We study the combination of face verification algorithms in order to improve the verification performance. Although the different face experts that we consider provide their output scores on the basis of the same image, they are complementary to each other and their combination, using a simple sum

The task of face verification is made more difficult when the illumination conditions of image capture are not constrained. The differences in illumination conditions between the stored images of the client and the probe image can be lessened by the application of photometric normalisation

This paper introduces a video based face verification system. Stereo vision and multiple-related template matching method are used to segment facial region from the background. Facial features are extracted for geometrical normalization and followed by illumination normalization. Faces

We develop a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (superclasses) to train a set of binary classifiers. The output

Abstract. Recent year, the method based on face verification has been one of the most vibrant study aspect in face verification field. The problem of face verification can be defined: imputing the picture or video, identifying or validating by using face database. “video-video ” face verification

subject-generated similarity scores were added to the PLS analysis. Fusing humans and algorithms increased performance to near-perfect classification accuracy. These results are discussed in terms of maximizing face verification accuracy with hybrid systems consisting of multiple algorithms and humans

We compare four local feature extraction techniques for the task of face verification, namely (ordered in terms of complexity): raw pixels, raw pixels with mean removal, 2D Discrete Cosine Transform (DCT) and local Principal Component Analysis (PCA). The comparison is performed in terms