Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition.
Comput Intell Neurosci
; 2018: 3803627, 2018.
Article
en En
| MEDLINE
| ID: mdl-30210533
ABSTRACT
Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and conveniently. Besides, it can generate samples such as disguise, expression, and mixed variation. Second, it uses transfer learning and introduces a well-trained deep convolutional neural network (DCNN) model and then selects some expanding samples to fine-tune the DCNN model. Third, the fine-tuned model is used to implement experiment. Experimental results on AR face database, Extend Yale B face database, FERET face database, and LFW database demonstrate that TDL achieves the state-of-the-art performance in SSPP FR.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Reconocimiento de Normas Patrones Automatizadas
/
Redes Neurales de la Computación
/
Cara
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Intell Neurosci
Asunto de la revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Año:
2018
Tipo del documento:
Article
País de afiliación:
China