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Few-shot learning with pre-trained layers integration applied to hand gesture recognition for people with disabilities

100%

Elbahri M. , Taleb N. , Ardjoun S. A. E. M. , Zouaoui C. M. A.

Applied Computer Science

2024

tom Vol. 20, no 2

1--23

Employing vision-based hand gesture recognition for the interaction and communication of disabled individuals is highly beneficial. The hands and gestures of this category of people have a distinctive aspect, requiring the adaptation of a deep learning vision-based system with a dedicated dataset for each individual. To achieve this objective, the paper presents a novel approach for training gesture classification using few-shot samples. More specifically, the gesture classifiers are fine-tuned segments of a pre-trained deep network. The global framework consists of two modules. The first one is a base feature learner and a hand detector trained with normal people hand’s images; this module results in a hand detector ad hoc model. The second module is a learner sub-classifier; it is the leverage of the convolution layers of the hand detector feature extractor. It builds a shallow CNN trained with few-shot samples for gesture classification. The proposed approach enables the reuse of segments of a pre-trained feature extractor to build a new sub-classification model. The results obtained by varying the size of the training dataset have demonstrated the efficiency of our method compared to the ones of the literature.

Tracking of dynamic gesture fingertips positionin video sequence

75%

Grzejszczak T. , Molle R. , Roth R.

Archives of Control Sciences

2020

tom Vol. 30, No. 1

101--122

The field of research of this paper combines Human Computer Interface, gesture recognition and fingertips tracking. Most gesture recognition algorithms processing color images are unable to locate folded fingers hidden inside hand contour. With use of hand landmarks detection and localization algorithm, processing directional images, the fingertips are tracked whether they are risen or folded inside the hand contour. The capabilities of the method, repeatibility and accuracy, are tested with use of 3 gestures that are recorded on the USB camera. Fingertips are tracked in gestures presenting a linear movement of an open hand, finger folding into fist and clenched fist movement. In conclusion, a discussion of accuracy in application to HCI is presented.

Tracking of dynamic gesture fingertips position in video sequence

63%

Grzejszczak T. , Molle R. , Roth R. , Grzejszczak T. , Molle R. , Roth R.