Deep learning for real-time robust facial expression recognition on a smartphone

Song, Inchul; Kim, Hyun-Jun; Jeon, Paul Barom

doi:10.1109/icce.2014.6776135

Cited by 89 publications

(36 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The logic of CNN is to train the network, the same way as a human learns things. Facial expression recognition system was developed in Reference , which runs on a smartphone using DCNN. The network proposed, composed of five layers and 65 000 neurons.…”

Section: Related Workmentioning

confidence: 99%

Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

2020

View full text Add to dashboard Cite

Summary Video semantic analysis (VSA) has received significant attention in the area of Machine Learning for some time now, particularly video surveillance applications with sparse representation and dictionary learning. Studies have shown that the duo has significantly impacted on the classification performance of video detection analysis. In VSA, the locality structure of video semantic data containing more discriminative information is very essential for classification. However, there has been modest feat by the current SR‐based approaches to fully utilize the discriminative information for high performance. Furthermore, similar coding outcomes are missing from current video features with the same video category. To handle these issues, we first propose an improved deep learning algorithm—locality deep convolutional neural network algorithm (LDCNN) to better extract salient features and obtain local information from semantic video. Second, we propose a novel DL method, called deep locality‐sensitive discriminative dictionary learning (DLSDDL) for VSA. In the proposed DLSDDL, a discriminant loss function for the video category based on sparse coding of sparse coefficients is introduced into the structure of the locality‐sensitive dictionary learning (LSDL) method. After solving the optimized dictionary, the sparse coefficients for the testing video feature samples are obtained, and then the classification result for video semantic is realized by reducing the error existing between the original and recreated samples. The experiment results show that the proposed DLSDDL technique considerably increases the efficiency of video semantic detection as against competing methods used in our experiment.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

2020

View full text Add to dashboard Cite

show abstract

“…Xiaoli et al [8] proposed the transformation of 3D facial information into 2D range image for representation and analyzed various approaches for generation of range image and features for 3D facial expression recognition. Inchul Song et al [9] used deep convolutional neural network with 5 layer to develop a face expression recognition system for smart-phone. Samira Ebrahimi Kahou et al [10] used deep convolutional neural network to analyze facial expression in video frames and other neural network architectures to recognize emotions in videos.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Kinect Depth Sensor and Convolutional Neural Networks

Ijjina

Mohan

2014

2014 13th International Conference on Machine Learning and Applications

View full text Add to dashboard Cite

Facial expression recognition is an active area of research with applications in the design of Human Computer Interaction (HCI) systems. In this paper, we propose an approach for facial expression recognition using deep convolutional neural networks (CNN) based on features generated from depth information only. The Gradient direction information of depth data is used to represent facial information, due its invariance to distance from the sensor. The ability of a convolutional neural networks (CNN) to learn local discriminative patterns from data is used to recognize facial expressions from the representation of unregistered facial images. Experiments conducted on EURECOM kinect face dataset demonstrate the effectiveness of the proposed approach.

show abstract

“…There are two central challenges in machine learning: underfitting and overfitting [38]. Overfitting occurs when the gap between the training error and test error is too large.…”

Section: Training and Learning Cnnsmentioning

confidence: 99%

Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

Namatēvs

2017

Information Technology and Management Science

View full text Add to dashboard Cite

-Deep convolutional neural networks (CNNs) are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

show abstract

Deep learning for real-time robust facial expression recognition on a smartphone

Cited by 89 publications

References 5 publications

Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

Facial Expression Recognition Using Kinect Depth Sensor and Convolutional Neural Networks

Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

Contact Info

Product

Resources

About