Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using Principle Component Analysis and recognition using the feed forward back propagation Neural Network. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. The Eigenface approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional space.

Face recognition using Eigenfaces

Despite extensive research conducted in machine vision for harvesting robots, practical success in this field of agrobotics is still limited. This article presents a comprehensive review of classical and state-of-the-art machine vision solutions employed in such systems, with special emphasis on the visual cues and machine vision algorithms used. We discuss the advantages and limitations of each approach and we examine these capacities in light of the challenges ahead. We conclude with suggested directions from the general computer vision literature which could assist our research community meet these challenges and bring us closer to the goal of practical selective fruit harvesting robots.

https://www.cs.bgu.ac.il/~ben-shahar/Publications/2012-Kapach_Barnea_Mairon_Edan_and_Ben_Shahar-Computer_Vision_for_Fruit_Harvesting_Robots.pdf

Computer vision for fruit harvesting robots state of the art and challenges ahead

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review.

http://www.jci-bioinfo.cn/papers/YJTBI5186.pdf

Predicting protein structural classes with pseudo amino acid composition : An approach using geometric moments of cellular automaton image

In this study we propose a new system to detect the object from an input image. The proposed system first uses the separability filter proposed by Fukui and Yamaguchi (Trans. IEICE Japan J80-D-II. 8, 2170-2177, 1997) to obtain the best object candidates and next, the system uses the Circular Hough Transform (CHT) to detect the presence of circular shape. The main contribution of this work consists of using together two different techniques in order to take advantages from the peculiarity of each of them. As the results of the experiments, the object detection rate of the proposed system was 96% for 25 images by moving the circle template every 20 pixels to right and down.

Object Detection using Circular Hough Transform

Geometric moment invariant produces a set of feature vectors that are invariant under shifting, scaling and rotation. The technique is widely used to extract the global features for pattern recognition due to its discrimination power and robustness. In this paper, moment invariant is used to identify the object from the captured image using the first invariant (O1). The recognition rate for this technique is 90% after the image undergoes suitable processing and segmentation process.

/pdf/object-detection-using-geometric-invariant-moment-2wkq5z7gxa.pdf

Object Detection using Geometric Invariant Moment

In this study, we develop a computational model to identify the face of an unknown person's by applying eigenfaces. The eigenfaces has been applied to extract the basic face of the human face images. The eigenfaces is then projecting onto human faces to identify unique features vectors. This significant features vector can be used to identify an unknown face by using the backpropagation neural network that utilized euclidean distance for classification and recognition. The ORL database for this investigation consists of 40 people with various 400 face images had been used for the learning. The eigenfaces including implemented Jacobi's method for eigenvalues and eigenvectors has been performed. The classification and recognition using backpropagation neural network showed impressive positive result to classify face images.

/pdf/face-recognition-using-eigenfaces-and-neural-networks-40zir28g8k.pdf

Face Recognition using Eigenfaces and Neural Networks

In this paper, a real time industrial machine vision system incorporating optical character recognition (OCR) is employed to inspect markings on integrated circuit (IC) chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instruments is used in the investigation. The IC chip markings are laser printed. This inspection system tests whether the laser printed marking on IC chips is proper. The inspection has to identify print errors such as illegible characters, missing characters and upside down printing. The vision inspection of the printed markings on the IC chip is carried out in three phases, namely, image preprocessing, feature extraction and classification. The MATLAB platform and its toolboxes are used for designing the inspection processing technique. Speed of the marking inspection is mostly dependent on the effectiveness of the feature extraction technique. The performances of four feature extraction techniques are compared in terms of their respective speed. The feature extracted data are used in a neural network for classifying the marking errors. A suggestion to optimize the number of input neurons of the neural network for a fast classification is also presented.

A real time marking inspection scheme for semiconductor industries

In this paper, lip features are applied to classify the human emotion using a set of irregular ellipse fitting equations using Genetic algorithm. As Japanese, is considered in this study. All six universally accepted emotions are considered for classifications. Lip is usually considered as one of the features for recognizing the emotion. In this work, three feature extraction methods are proposed and their respective performances are compared for determining the feature of the lips. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to unique characteristic of lips. GA is adopted to optimize such irregular ellipse characteristics of the lip features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotion. This has given reasonably successful emotion classifications for Japanese subject.

M. Karthigayan

Papers

Object Detection using Circular Hough Transform

Object Detection using Geometric Invariant Moment

Face Recognition using Eigenfaces and Neural Networks

A real time marking inspection scheme for semiconductor industries

Japanese Face Emotions Classification Using LIP Features