Abstract
Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset namely Labeled faces in the wild (LFW) and Olivetti Research Laboratory (ORL) dataset. In this paper, the feature extraction is based on local phase quantization with directional graph features for an effective optimal path and the geometric features. Further, Person Identification based deep neural network (PI-DNN) has proposed are expected to provide a high recognition rate. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves high-performance values when it is compared with other existing methods. The novelty of this paper explains in understanding the various features of different types of classifiers used. It is mainly developed to recognize the human faces in the crowd, and it is also deployed for criminal identification.
Highlights
Face recognition can be defined as biometric artificial intelligence relied on the application that is exclusively designated to recognize a person by exploring sequences based on the individual’s facial shape and texture
Numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification
The applied machine learning algorithm, such as Local Phase quantization with directorial graphbased features and Geometric based feature extraction have been applied in selected Labeled faces in the wild (LFW) and Olivetti Research Laboratory (ORL) datasets to perform effective human face recognition
Summary
Face recognition can be defined as biometric artificial intelligence relied on the application that is exclusively designated to recognize a person by exploring sequences based on the individual’s facial shape and texture. Directional graph-based and geometric based methods in feature extraction are applied to extract the facial feature in Region of interest (ROI) along with error filtering techniques to provide prominent face components. It is composed of multiple layers to detect necessary edges, intricate shapes, and by further processing, the final layer can catch the entire face and confirm it by verifying with the high dimensional dataset It is mostly preferred in real-time applications. In recent days Deep learning techniques are highly utilized in recognition of human face from the large datasets [4] By considering this the major contribution of the study involves, The applied machine learning algorithm, such as Local Phase quantization with directorial graphbased features and Geometric based feature extraction have been applied in selected LFW and ORL datasets to perform effective human face recognition.
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