Spatial Domain Representation for Face Recognition

Spatial domain representation for face recognition characterizes extracted spatial facial features for face recognition. This chapter provides a complete understanding of well-known and some recently explored spatial domain representations for face recognition. Over last two decades, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) and local binary patterns (LBP) have emerged as promising spatial feature extraction techniques for face recognition. SIFT and HOG are effective techniques for face recognition dealing with different scales, rotation, and illumination. LBP is texture based analysis effective for extracting texture information of face. Other relevant spatial domain representations are spatial pyramid learning (SPLE), linear phase quantization (LPQ), variants of LBP such as improved local binary pattern (ILBP), compound local binary pattern (CLBP), local ternary pattern (LTP), three-patch local binary patterns (TPLBP), four-patch local binary patterns (FPLBP). These representations are improved versions of SIFT and LBP and have improved results for face recognition. A detailed analysis of these methods, basic results for face recognition and possible applications are presented in this chapter

Face Detection and Recognition Using Raspberry PI Computer

This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset

Image-based family verification in the wild

Facial image analysis has been an important subject of study in the communities of pat- tern recognition and computer vision. Facial images contain much information about the person they belong to: identity, age, gender, ethnicity, expression and many more. For that reason, the analysis of facial images has many applications in real world problems such as face recognition, age estimation, gender classification or facial expression recognition. Visual kinship recognition is a new research topic in the scope of facial image analysis. It is essential for many real-world applications. However, nowadays there exist only a few practical vision systems capable to handle such tasks. Hence, vision technology for kinship-based problems has not matured enough to be applied to real- world problems. This leads to a concern of unsatisfactory performance when attempted on real-world datasets. Kinship verification is to determine pairwise kin relations for a pair of given images. It can be viewed as a typical binary classification problem, i.e., a face pair is either related by kinship or it is not. Prior research works have addressed kinship types for which pre-existing datasets have provided images, annotations and a verification task protocol. Namely, father-son, father-daughter, mother-son and mother-daughter. The main objective of this Master work is the study and development of feature selection and fusion for the problem of family verification from facial images. To achieve this objective, there is a main tasks that can be addressed: perform a compara- tive study on face descriptors that include classic descriptors as well as deep descriptors. The main contributions of this Thesis work are: 1. Studying the state of the art of the problem of family verification in images. 2. Implementing and comparing several criteria that correspond to different face rep- resentations (Local Binary Patterns (LBP), Histogram Oriented Gradients (HOG), deep descriptors)

Attendance Tracking by Facial Recognition

Current systems that are generally used for tracking attendance for online exams/courses is either manual or marked automatically by successful logins. The proposed system uses facial detection and recognition to mark the attendance. This can be further expanded to track employees, replace traditional paper attendance and so on. Facial recognition system also increases security apart from frauds, it ensures no accidental data is leaked to unauthorized persons and no human intervention is needed to monitor the attendance or registration. The proposed system allows pre-registration of users so that their details can be stored in the database, it also stores several sample images of the user’s face. The program compares the user’s face accessed whose attendance is sought to be marked in the real time through the laptop/computer’s in-built camera. This is compared with the samples stored in the database and if a match is found, the attendance is marked automatically. This technology is currently in use for high end security organizations, government offices, immigration services, etc. However, it is still not expanded to be used for general purposes as the skills to develop facial recognition systems were limited and expensive until recent developments in machine learning took place. The advancement in research for artificial neural networks (ANN) has provided us with the concept of deep learning which can be used interchangeably with ANN now-a-days. It dissects facial images into pixels and patterns of pixels. It then generates a general pattern for a particular image known as the histogram of Gradients and then runs the calculations for basic measurements of the features. Based on these calculations, algorithms like eigenfaces and Local Binary Patterns Histograms are used for further smarter processing

Automatic nesting seabird detection based on boosted HOG-LBP descriptors

Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution 1. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved. © 2011 IEEE

Automatic 3D facial expression recognition using geometric and textured feature fusion

3D facial expression recognition has gained more and more interests from affective computing society due to issues such as pose variations and illumination changes caused by 2D imaging having been eliminated. There are many applications that can benefit from this research, such as medical applications involving the detection of pain and psychological effects in patients, in human-computer interaction tasks that intelligent systems use in today's world. In this paper, we look into 3D Facial Expression Recognition, by investigating many feature extraction methods used on the 2D textured images and 3D geometric data, fusing the 2 domains to increase the overall performance. A One Vs All Multi-class SVM Classifier has been adopted to recognize the expressions Angry, Disgust, Fear, Happy, Neutral, Sad and Surprise from the BU-3DFE and Bosphorus databases. The proposed approach displays an increase in performance when the features are fused together

Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to Signal Processing: Image Communication journa