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http://dx.doi.org/10.12673/jant.2020.24.5.430

Implementation of Face Recognition Pipeline Model using Caffe  

Park, Jin-Hwan (Department of Energy IT, Gachon University)
Kim, Chang-Bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.24, no.5, 2020 , pp. 430-437 More about this Journal
Abstract
The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.
Keywords
Face detection; Face alignment; Embedding vector; Face recognition; Caffe;
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