Webcam‐based system for video‐oculography

Abstract

Video-oculography (VOG) is a tool providing diagnostic information about the progress of the diseases that cause regression of the vergence eye movements, such as Parkinson's disease (PD). The majority of the existing systems are based on sophisticated infra-red (IR) devices. In this study, the authors show that a webcam-based VOG system can provide similar accuracy to that of a head-mounted IR-based VOG system. They also prove that the authors' iris localisation algorithm outperforms current state-of-the-art methods on the popular BioID dataset in terms of accuracy. The proposed system consists of a set of image processing algorithms: face detection, facial features localisation and iris localisation. They have performed examinations on patients suffering from PD using their system and a JAZZ-novo head-mounted device with IR sensor as reference. In the experiments, they have obtained a mean correlation of 0.841 between the results from their method and those from the JAZZ-novo. They have shown that the accuracy of their visual system is similar to the accuracy of IR head-mounted devices. In the future, they plan to extend their experiments to inexpensive high frame rate cameras which can potentially provide more diagnostic parameters.

An Automated Framework for Patient Identification and Verification Using Deep Learning

“…Naruniec et al [28] have proposed an oculography system that contains four modules (i) face detection (ii) tracking the face after localization (iii) Iris localization (iv) Iris position regression. In this paper, the authors have examined Haar features, Histogram oriented Gradients (HoG) for face detection and simple probabilistic approach, logistic regression approach for classifying the face.…”

“…If the score is high, then the corresponding patient identity will be provided to the user. The applications of face recognition are patient identification and verification in medical emergencies [25], patient heart rate estimation [26], accessing out-patient information through electronic medical records [27], video oculography [28], and embedded security systems for accessing medical facilities [29]. Algorithms for Face recognition can be classified as Template and Geometric Feature-based approaches [30], Piecemeal and Holistic approaches [31], Statistical Method based approaches such as considering principal components [32], transformation technique [33], Linear combination of features [34], linear projective maps [35], Wavelet Transformation [36], Independent Component Analysis [37], Kernel Principal Component Analysis [38] and Neural Network-Based [39]; and View-Based & Modular Eigenfaces [40] approaches.…”

“…These existing conventional methods are failed to detect and recognize the face due to light variation, pose variations, occlusion, and background clutter. These methods have used features such as Haarcascade [41], Histogram Oriented Gradient [28], Local Binary Pattern [42], Eigenfaces [40], etc. to extract the facial features from the image.…”

Eye Blink Detection System for Paralyzed Patients

“…An algorithm was implemented for face detection, facial feature localisation and tracking, Iris localisation, Iris position registration [7].…”

Condução de cadeira de rodas usando rastreamento ocular por imagem

“…No intuito de aprimorar ainda mais a aquisição e processamento destes sinais foram desenvolvidas técnicas de vídeo-oculografia (VOG) [16][20] e oculografia por infravermelho (iROG) [16] [22], sendo que a última consiste na reflexão da pupila gerada pela emissão de feixes de luz infravermelha originados de um emissor na direção do olho do indivíduo [20] [22], tornando possível a detecção do olho com auxílio de uma câmera [16] [23]. No entanto, a maioria dos dispositivos atuais do mercado são sofisticados, chegando a exigir, em alguns casos que o usuário necessite ter um equipamento preso à cabeça [23], tornando-se inviáveis para o público alvo [9].…”