Deep Learning For Smile Recognition
Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUDeep LearningFacial Expression RecognitionFacial Expression Recognition (FER)GPUModel SelectionSmile RecognitionSimilar Papers 제목 키워드 기반
Smile detection in the wild based on transfer learning
Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which le…
4kFace RecognitionTransfer LearningRealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition
Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways. Several methods have been proposed to re…
Feature EngineeringSmile RecognitionDeep Convolutional Neural Networks for Smile Recognition
This thesis describes the design and implementation of a smile detector based on deep convolutional neural networks. It starts with a summary of neural networks, the difficulties of training them and new training methods…
CPUFacial Expression RecognitionFacial Expression Recognition (FER)GPU+2SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition
Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource languages but face significant challenges when applied to low-resource languages due to limited training data and insufficien…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data AugmentationIn-Context Learning+4Deep learning for identification and face, gender, expression recognition under constraints
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CN…
Smile Recognition