Machine learning for multi-view eye-pair detection

Karaaba, Mahir Faik; Schomaker, Lambert; Wiering, Marco A.

doi:10.1016/j.engappai.2014.04.008

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…This simpler method requires much less parameter tuning and much less computational time for training the models compared to deep learning architectures. Also many other feature extraction techniques have been used for different image recognition problems, such as principal component analysis (PCA) [9], restricted Boltzmann machines [14], and autoencoders [12].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Surinta

Karaaba

Mishra

et al. 2015

Engineering Applications of Neural Networks

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In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters. These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG features (HOG-BOW). These feature extraction algorithms are compared to other well-known techniques: principal component analysis, the discrete cosine transform, and the direct use of pixel intensities. The extracted features are given to three different types of support vector machines for classification, namely a linear SVM, an SVM with the RBF kernel, and a linear SVM using L2-regularization. We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets: Bangla, Odia and MNIST. The results show that the HOG-BOW, BOW and HOG method significantly outperform the other methods. The HOG-BOW method performs best with the L2-regularized SVM and obtains very high recognition accuracies on all three datasets.

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Section: Introductionmentioning

confidence: 99%

Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Surinta

Karaaba

Mishra

et al. 2015

Engineering Applications of Neural Networks

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“…In recent years, there has been an increase in the development of methods for detecting the center of the pupil using both traditional image processing techniques and machine learning-based approaches. Various algorithms for locating the eyes have been proposed, including the typical anthropometry-based method using standard measurements [10], the skin color model-based detection method [11], and statistical learning methods based on training data [12]. Among these, the statistical learning algorithm-based method is particularly popular in the current field of eye detection due to its high applicability and potential for further exploration in the areas of artificial intelligence and pattern recognition.…”

Section: Pupil Detection Technologymentioning

confidence: 99%

Measurement Method of Interpupillary Distance and Pupil Height Based on Ensemble of Regression Trees and the BlendMask Algorithm

Zhang

Huang

et al. 2023

Applied Sciences

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Measuring interpupilary distance and pupil height is a crucial step in the process of optometry. However, existing methods suffer from low accuracy, high cost, a lack of portability, and limited research on studying both parameters simultaneously. To overcome these challenges, we propose a method that combines ensemble regression trees (ERT) with the BlendMask algorithm to accurately measure both interpupillary distance and pupil height. First, we train an ERT-based face keypoint model to locate the pupils and calculate their center coordinates. Then, we develop an eyeglass dataset and train a BlendMask model to obtain the coordinates of the lowest point of the lenses. Finally, we calculate the numerical values of interpupillary distance and pupil height based on their respective definitions. The experimental results demonstrate that the proposed method can accurately measure interpupillary distance (IPD) and pupil height, and the calculated IPD and pupil height values are in good agreement with the measurements obtained by an auto-refractometer. By combining the advantages of the two models, our method overcomes the limitations of traditional methods with high measurement accuracy, low cost, and strong portability. Moreover, this method enables fast and automatic measurement, minimizing operation time, and reducing human errors. Therefore, it possesses broad prospects for application, particularly in the fields of eyeglass customization and vision inspection.

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“…For solution of this issue, we can rotate whole face by calculating distance from X and Y axis of both eyes. y = left eye from Y axis − right eye from Y axis x = left eye from X axisright eye from X axis Rotation angle = arctan (y/x) [7,8] Using above equation we have rotate whole face to prepare it straight then send for further steps. End of this step all faces are extracted and store it in form of feature vector for feature extraction.…”

Section: Face Features Vector Generationmentioning

confidence: 99%

Human recognition from multi angular images

Naik¹,

Lad²

Kalpa Publications in Computing

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Face recognition is still complicated task because to envision human actions might not realizable in each incident. Intention of face recognition is to identify human based on face that is similar from available dataset face images. Human face has multidimensional structure so it requires efficient technique for face harmonization and verification.Proposed work aim for developing efficient human face recognition method that deals with front as well as side view face in normal face expression. Using Viola–Jones face detection algorithm it accumulate only face region. Face features like eyes, nose and lip are extracted from whole face region using canny edge detection and harris corner detection method. To match individual face features, it compares position of edge boundary of features between images. Authors’ uses Euclidian distance method to retrieves maximum match value among all store face images. Based on threshold value it decides whether human face is recognized or not. Authors have evaluated performance of proposed method with DCT, DWD, PCA and LFL method on public free database like FEI, CVL and MIT-CBCL.

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Machine learning for multi-view eye-pair detection

Cited by 7 publications

References 16 publications

Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Measurement Method of Interpupillary Distance and Pupil Height Based on Ensemble of Regression Trees and the BlendMask Algorithm

Human recognition from multi angular images

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