Partial face recognition by template matching

2015 ◽  
Author(s):  
Naveena M ◽  
G HemanthaKumar ◽  
Prakasha M ◽  
P Nagabhushan
Keyword(s):  
Face Recognition ◽  
Template Matching

scholarly journals SISTEM PENGENALAN WAJAH MENGGUNAAN METODE TEMPLATE MATCHING

MATICS ◽  
2012 ◽  
Author(s):  
Yunifa Miftachul Arif ◽  
Achmad Sabar
Keyword(s):  
Face Recognition ◽  
Template Matching

Dewasa ini perhatian peneliti dalam memanfaatkan teknologi biometrik pada kehidupan untuk mengidentifikasi dan mengenal karakteristik manusia sudah banyak. Teknologi ini mengidentifikasi bagian tubuh manusia yang unik dan tetap seperti sidik jari, mata dan wajah manusia. Hal khusus di bidang identifikasi dan pengenalan wajah manusia memanfaatkan pengolahan dan analisis citra wajah, seperti menentukan daerah komponen wajah manusia dan karakteristiknya, yang akan membentuk suatu semantik wajah yang membantu mengungkapkan bagaimana komponen individu berperan dalam pengenalan wajah. Pada penelitian ini dikembangkan sistem yang memisahkan citra wajah ke dalam komponen wajah, kemudian mengekstraksinya ke dalam fitur mata dan batas wajah pada citra diam tunggal yang diambil dari posisi tampak depan. Antara tiap komponen diukur jaraknya, kemudian dikombinasikan dengan fitur lainnya untuk membentuk semantik wajah. Melalui tahapan deteksi wajah, berdasarkan model warna kulit dan normalisasi daerah wajah serta ekstraksi fitur mata, maka jarak masing-masing fitur dapat ditentukan. <br /><br />Kata kunci: Template Matching, Face Recognition, Pengenalan Wajah<br /><br />

scholarly journals Human Face and Facial Parts Detection using Template Matching Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 2296-2299
Keyword(s):  
Face Recognition ◽  
Template Matching ◽  
Cross Correlation ◽  
Human Face ◽  
Target Image ◽  
Matching Technique ◽  
Potential Applications ◽  
The Face ◽  
Execution Speed ◽  
Angle Interval

Face recognition has become relevant in recent years because of its potential applications. The aim of this paper is to find out the relevant techniques which give not only better accuracy also the efficient speed. There are several techniques available for face detection which give much better accuracy but the execution speed is not efficient. In this paper, a normalized cross-correlation template matching technique is used to solve this problem. According to the proposed algorithm, first different facial parts are detected likes mouth, eyes, and nose. If any of the two facial parts are found successfully then the face can be detected. For matching the templates with the target image, the template rotates at a certain angle interval.

FACE RECOGNITION IN COLOUR IMAGES USING NEURAL NETWORKS AND GENETIC ALGORITHMS

2005 ◽  
Vol 05 (01) ◽  
pp. 55-67 ◽  
Author(s):  
STEPHEN KARUNGARU ◽  
MINORU FUKUMI ◽  
NORIO AKAMATSU
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Template Matching ◽  
Distance Measure ◽  
Facial Feature ◽  
Training Data ◽  
Segmentation Method ◽  
Facial Feature Extraction ◽  
Matching Process ◽  
The Face

In this paper, a system that can automatically detect and recognise frontal faces is proposed. Three methods are used for face recognition; neural network, template matching and distance measure. One of the main problems encountered when using neural networks for face recognition is insufficient training data. This problem arises because, in most cases, only one image per subject is available. Therefore, amongst the objectives is to solve this problem by "increasing" the data available from the original image using several preprocesses, for example, image mirroring, colour and edges information, etc. Moreover, template matching is not trivial because of differences in the template shapes and sizes. In this work, template matching is aided by a genetic algorithm to automatically test several positions around the target and automatically adjust the size of the template as the matching process progresses. Distance measure method depends heavily on good facial feature extraction results. The image segmentation method applied matches such demand. The face colour information is represented using YIQ and the XYZ colour spaces. The effectiveness of the proposed method is verified by performing computer simulations. Two sets of databases were used. Database1 consists of 267 faces from the Oulu university database and database2 (for comparision purposes) consists of 250 faces from the ORL database.

scholarly journals Sistem Pengenalan Wajah 3D Menggunakan ICP dan SVM

2019 ◽  
Vol 6 (6) ◽  
pp. 601
Author(s):  
Ledya Novamizanti ◽  
Nadya Viana De Lima ◽  
Eko Susatio
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Template Matching ◽  
Confusion Matrix ◽  
Iterative Closest Point ◽  
Reference Image ◽  
Support Vector ◽  
Identification System ◽  
3D Face ◽  
3 Dimensional

<p>Pengenalan wajah merupakan salah satu teknologi biometrik yang banyak diaplikasikan terutama pada sistem keamanan. Sistem absensi dengan wajah, mengenali pelaku tindak kriminal dengan CCTV adalah beberapa aplikasi dari pengenalan wajah. Efisiensi dan akurasi menjadi faktor utama pengenalan wajah banyak diaplikasikan. Pada penelitian ini, sistem identifikasi<em> </em>diimplementasikan dalam bentuk pengenalan wajah 3 dimensi berbasis <em>t</em><em>emplate </em><em>m</em><em>atching </em>menggunakan metode<em> Iterative Closest Point</em> (ICP) dan klasifikasi <em>Support Vector Machine</em> (SVM). <em>Iterative Closest Point</em> (ICP) memberikan informasi dimensi dengan meminimalisasi kesalahan antara titik-titik dalam satu tampilan dan titik terdekatnya agar template wajah 3D yang dibuat sesuai dengan citra referensi. Sedangkan SVM adalah adalah metode klasifikasi dengan menentukan kelas citra berdasarkan informasi yang diperoleh dari proses ektraksi ciri.<em> </em>Hasil akhir dari penelitian ini adalah suatu aplikasi yang mampu melakukan identifikasi pengenalan pola wajah 3D. Berdasarkan <em>c</em><em>onfusion </em><em>m</em><em>atrix</em>, diperoleh bahwa sistem ini bekerja dengan <em>p</em><em>recision</em> 97,30%, <em>r</em><em>ecall</em> 100,00%, <em>a</em><em>ccuracy </em>97,56% pada pengambilan <em>frame</em> citra sebanyak 48, iterasi ke 49, partisi 12, dan menggunakan SVM tipe OAA.</p><p><em><strong>Abstract</strong></em></p><p><em>Face recognition is a biometric technology that is widely applied especially in the security system. Attendance systems with faces, recognizing criminals with CCTV are some of the applications of face recognition. Efficiency and accuracy are the main factors that face recognition is widely applied. In this study, the identification system was implemented in the form of 3-dimensional face recognition based on template matching using the Iterative Closest Point (ICP) method and Support Vector Machine (SVM) classification. Iterative Closest Point (ICP) provides dimensional information by minimizing errors between points in one view and the closest point so that 3D face templates are made in accordance with the reference image. Whereas SVM is a classification method by determining the image class based on information obtained from the extraction of features. The final result of this study is an application that is able to identify 3D face pattern recognition. Based on the confusion matrix, found that this system works with 97.30% precision, recall 100.00%, 97.56% accuracy in image frame capture as much as 48 iterations to 49, the partition 12, and using the SVM-type OAA.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Facial Features for Template Matching Based Face Recognition

2009 ◽  
Vol 6 (11) ◽  
pp. 1897-1901 ◽  
Author(s):  
Chai Tong Yuen ◽  
M. Rizon ◽  
Woo San San ◽  
Tan Ching Seong
Keyword(s):  
Face Recognition ◽  
Template Matching ◽  
Facial Features
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