Face Detecting Using Artificial Neural Network Approach

Nazeer, Shahrin Azuan; Omar, Norshafarina; Jumari, Khairol Faisal; Khalid, Marzuki

doi:10.1109/ams.2007.38

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Shahrin Azuan Nazeer, Nazaruddin Omar, Khairol Faisal Jumari and Marzuki Khalid (2007) [9] used ANN approach in face recognition. They evaluated the performance of the system by applying two photometric normalization techniques: histogram equalization and homomorphic filtering, and comparing with Euclidean Distance, and Normalized Correlation classifiers.…”

Section: Work Donementioning

confidence: 99%

Pattern Recognition Using Artificial Neural Network: A Review

Kim

2010

Communications in Computer and Information Science

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Abstract. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

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Section: Work Donementioning

confidence: 99%

Pattern Recognition Using Artificial Neural Network: A Review

Kim

2010

Communications in Computer and Information Science

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“…Some of the most effective examples of face recognition using ANN are: Convolutional Neural Networks (CNN), Evolutionary Optimization of Neural Networks, Multilayer Perceptron (MLP), Back Propagation Neural Networks (BPNN), Gabor Wavelet Faces with ANN, Skin Color and BPNN, etc. [35].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Uddin

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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Facial recognition research is one of the different types of research in this world today. In recent years, facial recognition in robots has attracted increased study interest. Robotic platforms now utilize a variety of object detection methods, with face detection being a viable use. Face detection in robotics is a computer technique that recognizes human faces in digital pictures and is used in a range of applications. Different authors have performed their research in different ways on the use of detection systems. This paper aims to give future researchers a better idea of using facial recognition systems in robotics. In this study, we reviewed research by various authors over recent years to facilitate future facial recognition research. In addition, scholars have addressed the topics, how they have done so, and the specifics of their approaches are described. This paper reviewed an overview of hardware implementation and software implementation by various authors. It can automatically focus cameras or count the number of people who have entered a location. Commercial applications of the method include displaying tailored advertisements in response to a recognized face along with the algorithms, functions and architectures used in facial recognition and giving the opinions of various authors mentioned. The comparative analysis of facial recognition and its architecture system is highlighted.

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“…The object detection method used in this project is a type known as the "single pass approach" [7,21,22,24] where only a single program sweeps through an image once. During one sweep, the program will attempt to gather all objects of interest.…”

Section: Object Detectionmentioning

confidence: 99%

“…There are many different features that could be used for an object detection system where some are simple and some are relatively complex, but exploring the effectiveness of these features is out of the scope of this project and we focus on using the simple ones that are used most commonly in past works [2,6,7,9,13,15,16,19]. In this experiment we use pixel intensity of an image to form two kinds of features; they are the mean of a region and the standard deviation of a region.…”

Section: Featuresmentioning

confidence: 99%

Object Detection Using Neural Networks and Genetic Programming

Chin

Zhang

2008

Lecture Notes in Computer Science

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Object detection has become an interesting and important research topic within computer science. Everyday we are facing increasing volumes of data where one of these types of data that is of great interest are images. This has led to the need for computers to search, recognise and classify relevant images for all forms of applications from military to civil. The fields of neural networks and genetic programming have achieved these goals with varying success. This project will investigate the task of effective and efficient object detection using neural networks and genetic programming. This project will begin by examining a standard neural network system and a standard genetic programming object detection system to determine which system is faster and what approaches should be taken in order to develop a more effective and more efficient object detection system. In the end this project will produce an improved approach that is more effective and more efficient than most traditional object detection systems.

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Face Detecting Using Artificial Neural Network Approach

Cited by 12 publications

References 8 publications

Pattern Recognition Using Artificial Neural Network: A Review

Pattern Recognition Using Artificial Neural Network: A Review

The Face Detection / Recognition , Perspective and Obstacles In Robotic: A Review

Object Detection Using Neural Networks and Genetic Programming

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