Face detection has attracted immense attention because it has many applications in computer vision communication and automatic control system. Face detection is a method to detect a face from an image which have several attributes in that image. Research into face detection, expression recognition, face tracking, pose estimation is required. By giving a single image, challenge is to detect the face from that image. Face detection is a challenging task because faces are not rigid and it changes in size, shape, color etc. Face detection become more challenging task when given image is not clear and occluded by any other thing and not proper lightning, not facing camera etc. In this paper, viola Jones algorithm is used to detect face and principal component analysis for face recognition.

An approach to face detection and recognition

A Systematic Literature Review of A* Pathfinding

Face recognition has become an attractive field in computer based application development in the last few decades. That is because of the wide range of areas they used in. And because of the wide variations of faces, face recognition from the database images, real data, capture images and sensor images is challenging problem and limitation. Image processing, pattern recognition and computer vision are relevant subjects to face recognition field. The innovation of new approaches of face authentication technologies is continuous subject to build much strong face recognition algorithms. In this work, to identify a face, there are three major strategies for feature extractions are discussed. Appearance-based and Modelbased methods and hybrid techniques as feature extractions are discussed. Also, review of major person recognition research the characteristics of good face authentication applications, Classification, Distance measurements and face databases are discussed while the final suggested methods are presented. This research has six sections organized as follow: Section one is the introduction. Section two is dedicated to applications related to face recognition. In Section three, face recognition techniques are presented by details. Then, classification types are illustrated in Section four. In section five, standard face databases are presented. Finally, in Section six, the conclusion is presented followed by the list of references.

A Review of Person Recognition Based on Face Model

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.

Facial expression recognition based on dual-feature fusion and improved random forest classifier

In our social life face is the primitive center for consideration, which plays a vital task for identification and an emotion. Live Face Recognition has attained enormously consideration in security systems, due to its non intrusiveness, accurate and fast results etc. Real time human Face recognition executes in two levels, such as Face detection and Face recognition. For face detection we are using viola-jones algorithm because of its high precision and high real time permit rate which is deployed in opencv by python language. In Face recognition two categories are considered: training phase and evaluation phase. In training phase, the algorithm is trained with the samples of the image to be learned and In estimation phase, the test image is compared among all the trained samples in the dataset. The facial features is extracted with the detected faces from live stream by Local Binary Patterns Histogram (LBPH). By using the LBPH, face recognition is finished using Euclidean distance classifier. The Experimental results getting after implementations and testing is of accuracy 85%-95%. Keywords— LBPH, Viola Jones, Euclidean Distance, OpenCV, Webcam

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Real Time Face Recognition of Human Faces by using LBPH and Viola Jones AlgorithmReal Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm

In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.

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Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance

Pathfinding algorithm addresses the problem of finding the shortest path from source to destination and avoiding obstacles. One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. In this work, A* search algorithm is used to find the shortest path between the source and destination on image that represents a map or a maze. Finding a path through a maze is a basic computer science problem that can take many forms. The A* algorithm is widely used in pathfinding and graph traversal. Different map and maze images are used to test the system performance (100 images for each map and maze). The system overall performance is acceptable and able to find the shortest path between two points on the images. More than 85% images can find the shortest path between the selected two points.

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Pathfinding in Strategy Games and Maze Solving Using A* Search Algorithm

A New Efficient Hybrid String Matching Algorithm to Solve the Exact String Matching Problem

License Plate (LP) is a rectangular metal plate located on vehicles body and contain numbers and characters or words for identification purposes. License Plate Recognition (LPR) system is a computer vision and image processing technology normally used to identify vehicles by their unique license plates. Over the last few years, the great importance of finding the exact accurate location of the license plate in an automatic manner has largely increased. This work basically aims to design an automatic number plate recognition (ANPR) system for the new Iraqi license plates using back propagation neural network. The simulation of the proposed system was implemented using MATLAB software package. The software is able to recognizes license plate number for vehicles, compares the license plate number with a built-in database and get information about the exact vehicle, and finally, decides whether or not this vehicle is allowed to enter a specific area. This software is able to recognize license plates when dealing with bad quality vehicle license plate images.

Automatic Iraqi license plate recognition system using back propagation neural network (BPNN)

In this paper, a system for face tracking and recognition in a video sequence is proposed based on KLT (Kanade-Lucas-Tomasi) tracker and 2DPCA (Two-Dimensional principal Component Analysis). Before using KLT algorithm for tracking faces, Viola-Jones face- detection-algorithm is applied to-detect all faces in the image or video sequence. KLT tracks face objects after being detected in the consecutive frames and sustaining long-term- tracking when faces come in/out. Face features are captured and selected using 2DPCA technique which is applied as feature extraction in order to eliminate noise and recognize faces more efficiently using a distance classifier. Face94 dataset and images captured by computer webcam are-used-to test the proposed system. Experimental results-show-that Viola-Jones algorithm is efficient when detect front faces. The KLT algorithm is tested to track faces using ten different videos captured by computer webcam. KLT is successfully applied and is able to track multiple faces even when the detected face turns left or right. Finally, 2DPCA is successfully applied and is able to recognize faces in both Face94 dataset and computer webcam video sequence.

Sinan Sameer Mahmood Al-Dabbagh

Papers

Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance

Pathfinding in Strategy Games and Maze Solving Using A* Search Algorithm

A New Efficient Hybrid String Matching Algorithm to Solve the Exact String Matching Problem

Automatic Iraqi license plate recognition system using back propagation neural network (BPNN)

Real-Time Face Tracking and Recognition System Using Kanade-Lucas-Tomasi and Two-Dimensional Principal Component Analysis