Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

Ansar, Hira; Jalal, Ahmad; Gochoo, Munkhjargal; Kim, Kibum

doi:10.3390/su13052961

Cited by 49 publications

(14 citation statements)

References 97 publications

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“…(2) GWO is novel method of swarm intelligence optimization. Grey wolves belong to Canidae family and are considered to be at the top of food chains, as apex predator [39]. They prefer to live as a group or in a pack.…”

Section: Mi(nk)mentioning

confidence: 99%

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Pervaiz¹,

Alsufyani²,

Jalal³

et al. 2023

Computers, Materials &Amp; Continua

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Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian's is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian's behavior detection. Kalman filter has been used to detect pedestrian's using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian's data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.

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Section: Mi(nk)mentioning

confidence: 99%

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Pervaiz¹,

Alsufyani²,

Jalal³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Secondly, if the positions of most of the faces in both datasets are not aligned properly this can be problematic for the detection of faces in the images. Thus, we set the face alignment of both the Gallagher collection person dataset and the Images of Groups dataset using the code available on GitHub [19]. For face detection, a skin color segmentation technique YCbCr is used.…”

Section: Pre-processing and Face Detectionmentioning

confidence: 99%

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

Rizwan¹,

Ghadi²,

Jalal³

et al. 2022

Computers, Materials &Amp; Continua

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With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age group. The proposed system is tested over two benchmark datasets, namely, the Gallagher collection person dataset and the Images of Groups dataset. The system achieved remarkable results over these benchmark datasets about recognition accuracy and computational time. The proposed system would also be applicable in different consumer application domains such as online business negotiations, consumer behavior analysis, E-learning environments, and emotion robotics.

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“…For hand gesture recognition, K-Ary Tree Hashing (KATH) classifier is used for the first time in our proposed model [64][65][66][67]. The KATH classifier takes the feature descriptors values of each image corresponding to the hand gesture and projects in a common space without the subtree pattern prior knowledge.…”

Section: Hand Gestures Recognitionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.

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Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

Cited by 49 publications

References 97 publications

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Automated Facial Expression Recognition and Age Estimation Using Deep Learning

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

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