Comparisons of features for automatic eye and mouth localization

Çevikalp, Hakan; Yavuz, Hasan Serhan; Edizkan, Rifat; Gunduz, Huseyin; Kandemir, Celal Murat

doi:10.1109/inista.2011.5946152

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…It's not an easy problem due to negative effects such as illumination differences, different exposure patterns etc. [36]. In this study, since the user takes place in front of the screen, the detection problem is the frontal face detection.…”

Section: Face and Fiducial Points Detectionmentioning

confidence: 99%

A human-computer interaction system based on eye, eyebrow and head movements

Tas¹,

Yavuz²

2022

Pamukkale J Eng Sci

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Devices like computers that require manual control are difficult to use by disabled people such as having multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), partial stroke, etc. These people have a very limited movement capability such that they can do most of the interaction with a limited movement of head and the eye. Assistive technologies are very important for people with disabilities not to be dependent on anyone in daily life. In this study, we present a humancomputer interaction application that works based on analysis of the head and eye/eyebrow movements from real-time images captured by a visual camera. We propose Difference Between Eye and Eyebrow (DEEB) features to detect the action intention of the user and properly realize the computer keyboard and mouse actions based on eyebrow, eye and head movements. In addition, there are shortcut keys in the designed interface that facilitate access to Instagram, WhatsApp, YouTube etc. which are considered to make it easier for individuals with disabilities to communicate with their social environment. We obtained satisfactory results on the designed interface in the experimental study. Manuel kontrol gerektiren bilgisayar gibi cihazların, Multipl Skleroz (MS), Amiyotrofik Lateral Skleroz (ALS), kısmi felç gibi engelli kişiler tarafından kullanılması zordur. Bu kişiler sınırlı hareket kabiliyetine sahip olduklarından çevresiyle iletişimlerinin çoğunu kısıtlı baş ve göz hareketleri ile gerçekleştirebilmektedirler. Engellilerin günlük hayatta kimseye bağımlı olmaması içinyardımcı teknolojiler oldukça önemlidir. Bu çalışmada, görsel kamera ile elde edilmiş gerçek zamanlı görüntülerden baş ve göz/kaş hareketlerinin analizine dayalı çalışan bir insan-bilgisayar etkileşimi uygulaması sunulmaktadır. Kullanıcının eylem niyetini tespit etmek ve kaş, göz ve baş hareketlerine göre bilgisayar klavyesi ve fare eylemlerini doğru bir şekilde gerçekleştirmek için Göz ve Kaş Arasındaki Fark (DEEB) özniteliği önerilmiştir. Ayrıca tasarlanan arayüzde, engelli bireylerin sosyal çevreleriyle iletişim kurmasını kolaylaştırdığı düşünülen Instagram, WhatsApp, YouTube vb. sosyal platformlara erişimlerini kolaylaştıran kısayol tuşları da bulunmaktadır. Deneysel çalışmada tasarlanan arayüzde tatmin edici sonuçlar elde edilmiştir.

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Section: Face and Fiducial Points Detectionmentioning

confidence: 99%

A human-computer interaction system based on eye, eyebrow and head movements

Tas¹,

Yavuz²

2022

Pamukkale J Eng Sci

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“…In a briefly explained manner, SVM [39][40][41][42][43] is presented as a two-class classifier and it finds the best subrplane which maximizes the distance between the best hyperplane and the closest sample to this hyperplane. If the training set is defined as TS = {( , 1 ), ( , 2 ), … , ( , )} for a two-class problem, a test vector (…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Therefore, it is also flexible for the aspects of lighting and expression. SVM determines the optimal subplane that maximizes the distance between the hyperplane and the closest sample from hyperplane [39][40][41][42][43]. In addition, CNN finds optimal set of weights adjusted to the smallest training error rate after feedforward propagation and feed-backward propagation stages.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition by Using 2D Orthogonal Subspace Projections

Ergin¹,

Işık²,

Gülmezoğlu³

2021

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In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.

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“…After the detection of the face region, we need to determine the coordinates of the center of the eyes of the face. This process is another challenging task [40]. For this purpose, we used one of the most efficient algorithms given in [41].…”

Section: Face Alignment Proceduresmentioning

confidence: 99%

A comprehensive comparison of features and embedding methods for face recognition

Yavuz¹,

Çevikalp²,

Edizkan³

2016

Turk J Elec Eng & Comp Sci

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Face recognition is an essential issue in modern-day applications since it can be used in many areas for several purposes. Many methods have been proposed for face recognition. It is a difficult task since variations in lighting, instantaneous mimic varieties, posing angles, and scaling differences can drastically change the appearance of the face. To suppress these complications, effective feature extraction and proper alignment of face images gain as much importance as the recognition method choice. In this paper, we provide an extensive comparison of the state-of-theart face recognition methods with the most well-known techniques used in feature representation. In order to test the performances of these various methods, we created a new face database, named the ESOGU face database, which includes frontal images of 100 people taken under different lighting and posing conditions. In addition to our new database, we also present experiments on the well-known AR face database to obtain more general and reliable results. Moreover, we investigate the automatic face detection and automatic normalization of the face images in the databases. Based on this, we discuss the use of such automatic methods for face recognition applications.

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Comparisons of features for automatic eye and mouth localization

Cited by 7 publications

References 17 publications

A human-computer interaction system based on eye, eyebrow and head movements

A human-computer interaction system based on eye, eyebrow and head movements

Face Recognition by Using 2D Orthogonal Subspace Projections

A comprehensive comparison of features and embedding methods for face recognition

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