Robust Real-Time Face Detection

Human intelligences can recollect and recognize a massive selection of faces, receiving a computer to do the same is challenging task but in modern domain there would be many practices of such systems. Face reorganization has been a fast increasing, challenging and interesting area in real time application. It can be extensively use for image and video processing; this requires computational models for the identification of the face. In this paper we will review the different methods for face recognition with Adaboost. This model should be easy and simple when implemented. Adaboost is based on haar cascade model and is quick and performs better in low resolution and complex background. It uses both approach of face detection i.e. image based and feature based. It is based on Robust Real Time Object Detection of Viola and Jone.

2010

Human face detection is an active area of research covering several disciplines such as image processing, pattern recognition and computer vision. This paper describes a face detection framework that is capable of processing input images pretty swiftly while achieving high detection rates. The existing methods for face detection can be divided into image based methods and feature based methods. The developed system is intermediary of these two, using a hybrid method comprising boosting algorithm and a hyper plane to train a classifier which is capable of processing images rapidly while having high detection rates. Using the response of simple Haar-based features used by Viola and Jones [1], AdaBoost algorithm and an additional hyper plane classifier, the presented face detection system is developed. This system is further modified by some intuitive noble heuristics. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Face recognition system is an application for identifying someone from image or videos. Face recognition is classified into three stages ie)Face detection,Feature Extraction ,Face Recognition. Face detection method is a difficult task in image analysis. Face detection is an application for detecting object, analyzing the face, understanding the localization of the face and face recognition.It is used in many application for new communication interface, security etc.Face Detection is employed for detecting faces from image or from videos. The main goal of face detection is to detect human faces from different images or videos.The face detection algorithm converts the input images from a camera to binary pattern and therefore the face location candidates using the AdaBoost Algorithm. The proposed system explains regarding the face detection based system on AdaBoost Algorithm . AdaBoost Algorithm selects the best set of Haar features and implement in cascade to decrease the detection time .The proposed System for face detection is intended by using Verilog and ModelSim,and also implemented in FPGA.

Lecture Notes in Computer Science, 2005

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.

Face detection is a very important biometric application in the field of image analysis and computer vision. The basic face detection method is AdaBoost algorithm with a cascading Haar-like feature classifiers based on the framework proposed by Viola and Jones. Real-time multiple-face detection, for instance on CCTVs with high resolution, is a computation-intensive procedure. If the procedure is performed sequentially, an optimal real-time performance will not be achieved. In this paper we propose an architectural design for a parallel and multiple-face detection technique based on Viola and Jones' framework. To do this systematically, we look at the problem from 4 points of view, namely: data processing taxonomy, parallel memory architecture, the model of parallel programming, as well as the design of parallel program. We also build a prototype of the proposed parallel technique and conduct a series of experiments to investigate the gained acceleration.

2012

This paper presents an architecture for face detection based system on AdaBoost algorithm using Haar features. We describe here design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Modelsim. Its performance has been measured and compared with an equivalent hardware implementation. We show about 35 time's increase of system performance over the equivalent hardware implementation.

—This paper describes about computer vision for localizing the faces and determining the sizes of faces in real-time in full-HD video. It is visualized through Machine Learning approach and the subsequent idea is to implement the same framework on the latest big data analytics tools for achieving face detection in online fashion. Here, Viola-Jones algorithm is considered for implementation. It is based on technique called Haar features. Different size and types of Haar features have resemblance to different facial regions representing distinct characteristics. Integral Image makes algorithm computationally efficient and AdaBoost reduces feature vector into only essential features that captures overall behavior. Cascading includes stages of classifiers to match the extracted features with actual characteristics and decision making about the face. Keywords—Face detection, Viola-Jones algorithm (Haar features + Integral Image + AdaBoost + Cascading)

2016

Face processing is one among the popular problems of detection and recognition. In surveillance system application, the massive data of video is analyzed by complex algorithms that must consider the real-time constraints. In this paper we propose a face processing framework as a component-based architecture for dealing with face online processing. Face detection and classification algorithms are realized based on a Haar-like features and Support Vector Machine (SVM) respectively as a way to efficiently extract a face image and enhance face recognition rates with robustness on different orientation Experimental results are presented with discussion. 1.

IEEE Transactions on Circuits and Systems for Video Technology, 2004

Automatic human face detection from images in surveillance and biometric applications is a challenging task due to the variances in image background, view, illumination, articulation, and facial expression. In this paper, we propose a novel three-step face detection approach to addressing this problem. The approach adopts a simple-to-complex strategy. First, a linear-filtering algorithm is applied to enhance detection performance by remove most nonface-like candidate rapidly. Second, a boosting chain algorithm is adopted to combine the boosting classifiers into a hierarchy "chain" structure. By utilizing the inter-layer discriminative information, this algorithm reveals higher efficiency than traditional approaches. Last, a postfiltering algorithm consists of image preprocessing; support vector machine-filter and color-filter are applied to refine the final prediction. As only small amount of candidate windows remain in the final stage, this algorithm greatly improves the detection accuracy with small computation cost. Compared with conventional approaches, this three-step approach is shown to be more effective and capable of handling more pose variations. Moreover, together with a two-level hierarchy in-plane pose estimator, a rapid multiview face detector is therefore built. The experimental results demonstrate the significant performance improvement using the proposed approach over others.