Comparative Analysis of Eigenface and Learning Vector Quantization (LVQ) to Face Recognition

Abstract

Face recognition is a topic most often discussed in this era because it can be applied and developed for several needs that can be useful in daily life. Face recognition always use learning method like eigenface and learning vector quantization (LVQ). The learning process is using the face of a digital image taken from a camera in five angles for one person that will apply for dataset learning (training and learning data set), and the live image is taken from the camera for testing (testing data image). The first step image will be detected with a haar cascade and captured by a camera. It will use to processing images to get the best image and prepare to be input into the network. From the experiments with 10 testings with various parameter values, the experiment results obtained the LVQ is more accurate than eigenface to identifying faces with average accuracy 66.29% and eigenface is 56.67% with comparison 9.62%, but eigenface is faster in running time with average time 4.39 seconds and 7.38 seconds for LVQ with comparison 2.98 seconds.

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

“…As one of the representative algorithms of deep learning, convolutional neural network imitates the visual mechanism of biology and has strong representational learning ability. A CNN is mainly composed of five structures: input layer, convolution layer, pooling layer, activation function, and full connection layer [ 28 , 29 ]. Each layer has multiple feature maps, each feature map extracts an input feature through a convolution filter, and each layer except full connection is only connected to some nodes of its upper layer.…”

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

“…Regarding the first evaluation index, both the sparse representation-based method [32] and the principal component analysis (PCA)-based technique [33] are considered for face recognition because of their fast computation speeds, the small requirements of the training dataset, and the convenient hardware implementation for our application. The sparse representation-based method exploits the discriminative nature of sparse representation to perform classification.…”

Pengenalan Ekspresi Wajah Pengemudi Berbasis Fitur Eigenface dan Gray Level Co-Occurance Matrice

“…1) Eigenface: Pada umumnya pendekatan eigenface digunakan untuk melakukan ekstraksi fitur wajah dalam melakukan proses pengenalan wajah maupun pengenalan ekspresi wajah dan dapat memberikan hasil kinerja yang sangat baik [11]. pendekatan eigenface menggunakan sejumlah parameter dalam melakukan proses ekstraksi fitur wajah diantaranya average image value (persamaan 1), covariance matrices (persamaan 2), mean image value (persamaan 3), dan eigenvalue dan eigenvalue (persamaan 4) [12], seperti yang ditunjukkan pada persamaan berikut :…”