Facial Expression Recognition with LBP and ORB Features

Abstract

Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.

Linear fusion approach to convolutional neural networks for facial emotion recognition

“…This can lead to mistakes in classification, particularly if there is just a small subset of traits that are helpful for classification. [30] 88.55% Fisher face [31] 89.2% Deep Features + HOG [32] 90.58% Sun et al [33] 92.00% CNN [34] 76.5% Proposed Model 92.4% LBP+ORB features [30] Deep features + HOG [32] Inception [35] Dynamic cascade classifier [36] Attentional convolutional [37] Proposed Model…”

BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için Derin Özellik Seçimi

“…Appearance-based methods are the use of features obtained from image processing techniques and calculations on pixel values. Methods such as Local Binary Patterns (LBP) [22][23][24], Histogram of Oriented Gradients (HOG) [25], Gabor Filters [26,27], Local Phase Quantization (LPQ) [28], Scale-Invariant Feature Transform (SIFT) [29,30] [31].…”

Patch-SIFT: Enhanced feature descriptor to learn human facial emotions using an Ensemble approach

“…Results of coupling a CNN model with well-tuned parameters in (7) achieves 97.6% and 94.5% accuracy rates respectively in original samples and 2DPCA based features in Yale dataset. Local Binary Pattern (LBP) and Oriented Fast and Rotated Brief (ORB) are combined as one of the feature descriptor to recognize facial expressions in (8) . The study in (8) (9) for emotion recognition with an average recognition rates of 94.75% and 96.86%, JAFFE and CK+ respectively.…”

“…Local Binary Pattern (LBP) and Oriented Fast and Rotated Brief (ORB) are combined as one of the feature descriptor to recognize facial expressions in (8) . The study in (8) (9) for emotion recognition with an average recognition rates of 94.75% and 96.86%, JAFFE and CK+ respectively. An Appearance Network and Geometric Network is combined to form a Deep Joint Spatiotemporal Network (DJSTN) (10) in which the authors have applied a 3D convolution on Face images to extract spatial and temporal features.…”