Facial recognition: Traditional methods vs. methods based on deep learning
Abstract:
The present study shows the quantitative analysis of the evaluation of traditional methods of facial recognition versus the evaluation of methods based on deep learning, the contribution of this study lies in the conditions of the dataset to be used, which unlike the regular paradigms, it contains a limited size of images to train what the researcher has called a hostile training environment, which will serve as a reference to determine what technique and/or algorithm can work to recognize faces in which you do not have a lot of information to be processed and with a poor quality of the face image to be recognized, factors that in recent years are recurring in the search requests of missing persons or in the absence of the quality of certain security cameras.
Año de publicación:
2020
Keywords:
- Eigenfaces
- Histograms
- KERAS
- Facial recognition
- OpenFace
- FISHERFACES
- Facenet
- TensorFlow
Fuente:


Tipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Visión por computadora
- Ciencias de la computación
Áreas temáticas:
- Ciencias de la computación
- Religión
- Métodos informáticos especiales