Expression Invariant Face Recognition Using Convolutional Neural Networks
نویسندگان
چکیده
منابع مشابه
Human Face Recognition Using Convolutional Neural Networks
In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorit...
متن کاملAge Invariant Face Recognition Using Convolutional Neural Networks and Set Distances
Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated ...
متن کاملFace Recognition across Time Lapse Using Convolutional Neural Networks
Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architectu...
متن کاملRobust Face Alignment Using Convolutional Neural Networks
Face recognition in real-world images mostly relies on three successive steps: face detection, alignment and identification. The second step of face alignment is crucial as the bounding boxes produced by robust face detection algorithms are still too imprecise for most face recognition techniques, i.e. they show slight variations in position, orientation and scale. We present a novel technique ...
متن کاملConvolutional Neural Networks for Facial Expression Recognition
We have developed convolutional neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study. We trained CNN models with different depth using gray-scale images. We developed our models in Torch [2] and exploited Graphics Processing Unit (GPU) computation in order to expedite th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IJARCCE
سال: 2019
ISSN: 2319-5940,2278-1021
DOI: 10.17148/ijarcce.2019.8538