Evaluation of different features for face recognition in video
نویسندگان
چکیده
With man One of the most critical tasks in automated face recognition technology is the extraction of facial features from a facial images. The most critical task in each face recognition (FR) technology, which contributes the most to the success of particular FR products in particular applications and which is highly protected by industries developing those products, is the extraction of facial features from a facial image. This report presents the performance comparison of several publicly reported feature extraction algorithms for face recognition in video. The evaluated features are Harris corner detection features, FAST (Features from Accelerated Segment Test), GFTT (Good Features To Track), MSER (Maximally Stable Extremal Regions), and HOG (Histograms of Oriented Gradients).
منابع مشابه
Facial Expression Recognition Based on Anatomical Structure of Human Face
Automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and hum...
متن کاملFace Recognition using an Affine Sparse Coding approach
Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...
متن کاملVideo-based face recognition in color space by graph-based discriminant analysis
Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining the three color component images. In this work, we consider grayscale image as well as color s...
متن کاملFace Recognition by Cognitive Discriminant Features
Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These...
متن کاملRecognition of Visual Events using Spatio-Temporal Information of the Video Signal
Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approac...
متن کاملFace Recognition using Eigenfaces , PCA and Supprot Vector Machines
This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...
متن کامل