Introduction to the First IEEE Workshop on Face Processing in Video

Gorodnichy, Dmitry O.

doi:10.1109/cvpr.2004.373

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…Pengenalan wajah manusia menggunakan kumpulan citra diam atau video dengan satu set video (Zhou et al, 2003). Penggunaan video-kamera dan komputer sudah cukup baik untuk memproses video secara waktu-nyata (real-time) (Gorodnichy, 2004).…”

Section: Pendahuluanunclassified

Pengenalan Wajah dengan Matriks Kookurensi Aras Keabuan dan Jaringan Syaraf Tiruan Probabilistik

Putra¹,

Adi

Isnanto

2013

J. Sistem Info. Bisnis

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Face recognition system is a development of the basic methods of authentication system using the natural characteristics of the human face as a baseline. Face recognition process consists of several phases, training and testing phase. The testing phase is done directly and indirectly. Indirect data test taken from a set of face images that have been selected, while direct data test take face image from camera. Human face recognition combines Gray Level Co-Occurrence Matrix/GLCM and Probabilistic Neural Network/PNN methods. Preprocessing is done by converting RGB to grayscale, using centroid method as face image segmentation process. Face recognition includes some factors, i.e. lighting, distance, angle and position. GLCM uses statistic method and second-order texture analysis, which represents image texture in following parameters energy, corelation, homogenity and contrast. While PNN is used to build database which is stored in the network in order to compare outcome from GLM in the form of matrix. This research uses face image as database by collecting sample of 10 persons, 5 face position, 2 type of distance shooting and 3 type of lighting. Testing process results 92% in direct recognition and 93,33% in inderict recognition.

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Section: Pendahuluanunclassified

Pengenalan Wajah dengan Matriks Kookurensi Aras Keabuan dan Jaringan Syaraf Tiruan Probabilistik

Putra¹,

Adi

Isnanto

2013

J. Sistem Info. Bisnis

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“…Furthermore, human face recognition by using a collection of still images or videos with a set of videos (Zhou, 2004). With the use of video cameras and computers it is good enough to process video in real-time (Gorodnichy, 2004) face recognition uses a camera to capture a person's face and after that compared to a face that has previously been stored in a database in real-time (Bayu, 2009). The use of the Gray Level Co-insurance Matrix (Gray Level Co-Occurrence Matrix / GLCM feature extraction) is mostly done in taking remote sensing imagery with prototypes (Maheshwary, 2009).…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition

Sukiman¹,

Suwilo²,

Zarlis³

2019

SinkrOn

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The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.

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“…when the objects is of unknown colour or shape) and/or when the camera is stationary with respect to the background. This is the method that is most commonly used for surveillance applications [7]. The preferable techniques for this method include nonlinear change detection techniques [6], which consider a pixel changed based on the patch around the pixel rather than the pixel itself, and statistical background modeling (such as the Mixtures of Gaussians technique) that learns the values of the background pixels over time.…”

Section: General Object Detection Rulesmentioning

confidence: 99%