A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

Learning Local Features Using Boosted Trees for Face Recognition

Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method to transform face images from any given lighting condition to a reference lighting condition using color constancy. Finally we propose methods to extract local features and train classifiers using these features. We developed two algorithms using these local features, modular PCA (Principal Component Analysis) and boosted tree. We present experimental results which show local features improve recognition accuracy when compared to accuracy of methods which use global features. The boosted tree algorithm recursively learns a tree of strong classifiers by splitting the training data in to smaller sets. We apply this method to learn features on the intrapersonal and extra-personal feature space. Once trained each node of the boosted tree will be a strong classifier. We used this method with Gabor features to perform experiments on benchmark face databases. Results clearly show that the proposed method has better face recognition and verification accuracy than the traditional AdaBoost strong classifier

Fast Face Detector Training Using Tailored Views

Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under con-trolled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core imple-mentation of Viola Jones ’ AdaBoost object detection frame-work. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art approaches on this challenging dataset. 1

Machine Analysis of Facial Expressions

No abstract

Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System

Automatic face detection is mainly the first step for most of the face-based biometric systems today such as face recognition, facial expression recognition, and tracking head pose. However, face detection technology has various drawbacks caused by challenges in indoor and outdoor environment such as uncontrolled lighting and illumination, features occlusions and pose variation. This thesis proposed a technique to detect multiface in video surveillance application with strategic architecture algorithm based on the hierarchical and structural design. This technique consists of two major blocks which are known as Face Skin Localization (FSL) and Hierarchical Skin Area (HSA). FSL is formulated to extract valuable skin data to be processed at the first stage of system detection, which also includes Face Skin Merging (FSM) in order to correctly merge separated skin areas. HSA is proposed to extend the searching of face candidates in selected segmentation area based on the hierarchical architecture strategy, in which each level of the hierarchy employs an integration of Adaboost and Neural Network Algorithm. Experiments were conducted on eleven types database which consists of various challenges to human face detection system. Results reveal that the proposed H-SKANN achieves 98.03% and 97.02% of of averaged accuracy for benchmark database and surveillance area databases, respectively

Enhanced Face Detection Based on Haar-Like and MB-LBP Features

The effective real-time face detection framework proposed by Viola and Jones gained much popularity due its computational efficiency and its simplicity. A notable variant replaces the original Haar-like features with MB-LBP (Multi-Block Local Binary Pattern) which are defined by the local binary pattern operator, both detector types are integrated into the OpenCV library. However, each descriptor and its evaluation method has its own set of strengths and setbacks. In this paper, an enhanced two-layer face detector composed of both Haar-like and MB-LBP features is presented. Haar-like features are employed as a coarse filter but with a new evaluation involving dual threshold. The already established MB-LBPs are arranged as the fine filter of the detector. The Gentle AdaBoost learning algorithm is deployed for the training of the proposed detector to reach the classification and performance potential. Experiments show that in the early stages of classification, Haar features with dual threshold are more discriminative than MB-LBP and original Haar-like features with respect to number of features required and computation. Benchmarking the proposed detector demonstrate overall 12% higher detection rate at 17% false alarm over using MB-LBP features singly while performing with ×3 speedup