Fusion Based FastICA Method: Facial Expression Recognition

Abstract

With the continuous progress of human computer interaction, face detection as well as facial expression recognition is gaining the attention of researchers from the fields of security, psychology, image processing, and computer vision. In this area the most challenging thing is to recognize accurate facial expression with minimum time requirement. In this work, our main focus is to minimize the time using fusion based Independent Component Analysis (ICA). Research studies show ICA has significant success on face image analysis. Among several architectures of ICA we mainly used here Gaussian kernel based FastICA algorithm due to time efficiency. We apply FastICA on whole faces to recognize facial expressions. Also we apply FastICA on different facial parts, by proposing two algorithms namely WAPA-FastICA and OEPA-FastICA, to analyze the influence of different parts for several basic emotions. Our experiment shows OEPA-FastICA and WAPA-FastICA outperforms existing predominant FastICA algorithm. We also compared these proposed algorithms with our previous PCA based facial expression recognition work. 

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

“…We evaluated seven post-processing schemes applied to ten feature extraction-based algorithms, using average F-score as the primary evaluation metric. In the first set of experiments, we measured the performance of filtered dilation (FiltDil) [22], heuristic filtering (HeurFilt) [23], sieving and opening (S&O) [25], sieving and closing (S&C) [3,4], the three-stage (3Stage) scheme [13], the enhanced three-stage (Enh3Stage) scheme [20] and spatial processing scheme (SpatialProc) [14] applied to the ten algorithms [25,[27][28][29][30][31][32][33][34][35] adapted for aerial vehicle detection. Randomly grouped frames were used for preliminary tests using two aerial image datasets (Tucson and Phoenix).…”

“…In the third set of experiments, we investigated the selection of post-processing schemes to combine with an 11th algorithm, which was a machine learning approach for vehicle detection. Inspired by some similar approaches such as spatial analysis [36] and smoke vehicle detection [37,38], besides the ten detection algorithms [25,[27][28][29][30][31][32][33][34][35], we adapted a two-stage machine learning approach from the tiramisu code [39] for semantic segmentation to perform the classification task via a hundred-layer densely connected convolutional network (DenseNets). The three post-processing schemes [4,14,20] that performed best in the first set of experiments were each applied to the machine learning approach.…”

“…Our quantitative results were obtained via two sets of evaluation metrics: basic information retrieval (IR) metrics [40] and the CLEAR metrics [41,42], where the former includes precision, recall, F-score, accuracy and percentage of wrong classification (PWC), and the latter comprises multiple object detection accuracy (MODA) and multiple object counts (MOC). For the fairness of comparing each detection to the ground truth (GT), we did not adopt the metric of multiple object detection precision [19] since the selected post-processing methods did not implement temporal analysis, and the eleven algorithms we adapted on aerial vehicle detection [25,[27][28][29][30][31][32][33][34][35]39] lack spatial overlap information.…”

A joint feature aggregation method for robust masked face recognition

“…The human face is one of the most discriminative biometrics and becomes a key feature for personal identification [1][2][3][4][5][6][7][8][9][10] . Recently, the pandemic of COVID-19 has also pushed the development of masked face recognition since wearing a mask is an effective way to defend against the virus and masked face recognition is a hot topic [11][12][13][14][15] .…”

Overlapped Semantic Age Group Estimation Using Hybrid PCA and Log Gabor Filter

“…So, we have undertaken East West College (EWC) and MORPH dataset for age estimation analysis. Regardless of these present difficulties, age estimation can be used in a wide range of smart human-machine applications, for an instant, limiting access to age-appropriate internet or television contents [9].…”