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The importance of embedded applications on image and video processing domain has been taking a larger space. The image processing is the study of several algorithms that takes an image as input and returns an image as output. Specially we... more

The importance of embedded applications on image and video processing domain has been taking a larger space. The image processing is the study of several algorithms that takes an image as input and returns an image as output. Specially we would like to elaborate our experience on the significance of computer vision as one of the domains where hardware implemented algorithms executes far better than those implemented from end to end software. Face detection is becoming one of the maximum interesting topics in the computer vision literature. The survey is directed to examine the face detection techniques. Image processing techniques is very widely held at present-day for face detection and gender detection. In this study, face detection technique is used for detecting and counting the number of passengers in electric vehicle over webcam. The webcam is installed in electric vehicle and attached with Raspberry Pi 2 model B. When electric vehicle run off from the station, webcam will capture passenger's images in the seating area. The images will be adjusted and enhanced to decrease the noise which is concluded by software application. The images are sent in the direction of the server by 3G communication. Then and there, the server progression the images by means of face detection technology and counting the number of passengers in electric vehicle. The system gets the supreme amount of passengers in electric vehicle that procedure over the images then evaluates the seat vacancy of the electric vehicle. Nikita A. Rekhate Dr. V. S. Gulhane'Image Processing Application for Vehicle Seat Vacancy Identification' Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12797.pdf

ABSTRACT Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear... more

ABSTRACT Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC). Experimental results demonstrate successful face detection over a wide range of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.

The ability to identify faces is of critical importance for normal social interactions. Previous evidence suggests that early visual deprivation may impair certain aspects of face recognition. The effects of strabismic amblyopia on face... more

The ability to identify faces is of critical importance for normal social interactions. Previous evidence suggests that early visual deprivation may impair certain aspects of face recognition. The effects of strabismic amblyopia on face processing have not been investigated previously. In this study, a group of individuals with amblyopia were administered two tasks known to selectively measure face detection based on a Gestalt representation of a face (Mooney faces task) and featural and relational processing of faces (Jane faces task). Our data show that--when relying on their amblyopic eye only - strabismic amblyopes perform as well as normally sighted individuals in face detection and recognition on the basis of their single features. However, they are significantly impaired in discriminating among different faces on the basis of the spacing of their single features (i.e., configural processing of relational information). Our findings are the first to demonstrate that strabismic amblyopia may cause specific deficits in face recognition, and add to previous reports characterizing visual perceptual deficits associated in amblyopia as high-level and not only as low-level processing.

ABSTRACT A modular architecture for real–time feature–based tracking is presented. This architecture takes advantage of temporal and spatial information contained in a video stream, combining ro-bust classifiers with motion estimation to... more

ABSTRACT A modular architecture for real–time feature–based tracking is presented. This architecture takes advantage of temporal and spatial information contained in a video stream, combining ro-bust classifiers with motion estimation to achieve real–time per-formance. The ...

This paper presents the implementation of a real-time face tracker to study the integration of Support Vector Machines (SVM) classi-fiers into a visual real-time tracking architecture. Face tracking has a large number of applications,... more

This paper presents the implementation of a real-time face tracker to study the integration of Support Vector Machines (SVM) classi-fiers into a visual real-time tracking architecture. Face tracking has a large number of applications, especially in the fields of surveil-lance and human-...

ABSTRACT Interactive mobile robots require object/subject detection in very visually complex environments. In the field of computer vision, specially when applied to robotics, several approaches like face detection, face recognition and... more

ABSTRACT Interactive mobile robots require object/subject detection in very visually complex environments. In the field of computer vision, specially when applied to robotics, several approaches like face detection, face recognition and pedestrian detection often have to deal with issues associated to bad illumination and strong featured background. These issues imply lack of performance because human detection algorithms will frequently process the whole image searching for features. Also, background segmentation approaches are commonly used to solve this problem on static camera surveillance. However all these approaches are unable to effectively deal with the constant background changes that certainly happen when the camera sensor is installed on a mobile robot. Hence, in this work we propose a Horopter based Dynamic Background Segmentation solution to this problem. Results show that our approach, significantly enhanced tracking, and consequently improved movement classification towards interaction.

Several methods for detecting the face and extracting the facial features and components exist in the literature. These methods are different in their complexity, performance, type and nature of the images and the targeted application.... more

Several methods for detecting the face and extracting the facial features and components exist in the literature. These methods are different in their complexity, performance, type and nature of the images and the targeted application. The facial features and components are used in security applications, robotics and assistance for the disabled. We use these components and characteristics to determine the state of alertness and fatigue for medical diagnoses. In this work we use plain color background images whose color is different from the skin and which contain a single face. We are interested in FPGA implementation of this application. This implementation must meet two constraints, which are the execution time and the FPGA resources. We have selected and have associated a face detection algorithm based on the skin detection (using the RGB space) with a facial-feature extraction algorithm based on tracking the gradient and the geometric model.

An automatic face annotation method is provided. The method includes dividing an input video into different sets of frames, extracting temporal and spatial information by employing camera take and shot boundary detection algorithms on the... more

An automatic face annotation method is provided. The method includes dividing an input video into different sets of frames, extracting temporal and spatial information by employing camera take and shot boundary detection algorithms on the different sets of frames, and collecting weakly labeled data by crawling weakly labeled face images from
Social networks. The method also includes applying face detection together with an iterative refinement clustering algorithm to remove noise of the collected weakly labeled data, generating a labeled database containing refined labeled images, finding and labeling exact frames containing one or more face images in the input video matching any of the refined labeled images based on the labeled database, labeling
remaining unlabeled face tracks in the input video by a semi Supervised learning algorithm to annotate the face images in the input video, and outputting the input video containing the annotated face images.

In this paper person identification is done based on sets of facial images. Each facial image is considered as the scattered point of logistic regression. The vertical distance of scattered point of facial image and the regression line is... more

In this paper person identification is done based on sets of facial images. Each facial image is considered as the scattered point of logistic regression. The vertical distance of scattered point of facial image and the regression line is considered as the parameter to determine whether the image is of same person or not. The ratio of Euclidian distance (in terms of number of pixel of gray scale image based on ‘imtool’ of Matlab 13.0) between nasal and eye points are determined. The variance of the ration is considered another parameter to identify a facial image. The concept is combined with ghost image of Principal Component Analysis; where the mean square error and signal to noise ratio (SNR) in dB is considered as the parameters of detection. The combination of three methods, enhance the degree of accuracy compared to individual one.

We introduce a face detector for wearable computers that exploits constraints in face scale and orientation imposed by the proximity of participants in near social interactions. Using this method we describe a wearable system that... more

We introduce a face detector for wearable computers that exploits constraints in face scale and orientation imposed by the proximity of participants in near social interactions. Using this method we describe a wearable system that perceives “social engagement,” i.e., when the wearer begins to interact with other individuals. Our experimental system proved >90% accurate when tested on wearable video data

Pengenalan wajah adalah teknologi komputer untuk menentukan lokasi wajah, ukuran wajah, deteksi fitur wajah dan pengabaian citra latar, selanjutnya dilakukan identifikasi citra wajah[1]. Mesin absensi kartu Amano GX500 banyak memiliki... more

Pengenalan wajah adalah teknologi komputer untuk menentukan lokasi wajah, ukuran wajah, deteksi fitur wajah dan pengabaian citra latar, selanjutnya dilakukan identifikasi citra wajah[1]. Mesin absensi kartu Amano GX500 banyak memiliki kekurangan. Seperti sistem penggajian perminggu yang harus dilakukan dengan menyalin jam-jam masuk dari kartu dan mengolahnya didalam microsoft excel hal ini tentu memakan waktu lama dan rawan human error. Berdasarkan masalah tersebut penulis melaksanakan penelitian yang bertujuan untuk merancang dan membuat sistem absensi berbasis pengenalan wajah menggunakan metode eigenface sebagai pengganti sistem absensi kartu.
Penelitian diawali dengan pengumpulan referensi yang berkaitan dengan sistem absensi pengenalan wajah, kemudian berdasarkan referensi tersebut dibuat aplikasi deteksi dan pengenalan wajah untuk menguji akurasi dari sistem. Setelah itu sistem absensi berbasis pengenalan wajah kemudian dibuat untuk diuji di CV. Karya Mitra Utama sebagai pengganti sistem absensi berbasis kartu. Hasil dari pengujian berupa Persentase nilai akurasi sistem adalah 97,22%. Dimana keluaran aplikasi berupa nilai gaji karyawan dalam format Hal ini menandakan bahwa aplikasi dapat digunakan sebagai aplikasi sistem absensi sekaligus dapat mempercepat proses pengolahan data absensi karyawan.

Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in... more

Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. 1. Introduction Most face recognition and tracking techniques employed in surveillance and human-computer interaction (HCI) systems rely on the assumption of a frontal view of the human face. In alternative approaches, knowledge of the orientation angle of the face in captured images can improve the performance of techniques based on non-frontal face views. It broadly consists of three parts: first, the face is detected by Haar detection based face detection method; then the face is tracked robustly using four extracted facial features; and finally, the orientation of the face is estimated by using the tracking results obtained independently from the three trackers (Viola and Jones; 2001). First, we use Haar detection method to identify a face area from any picture. The accuracy of this methodology is higher than 90% and very reliable to detect faces, we develop a new algorithm for face orientation recognition. The algorithm based on the combination of four individual tracking-based face orientation estimators that are relied on the seven properties of the face in question respectively: the variation of face regions,