A Novel Method for CNN Training Using Existing Color Datasets for Classifying Hand Postures in Bayer Images

Abstract

Deep learning has revolutionized the computer vision by achieving human level performance on classification and detection of the objects in the images. These algorithms are often trained using supervised learning methods which require a labeled training data set. Since the labeling requires human intervention, the human interpretable tri-color images are needed for this purpose even though the image sensors capture subsampled images in Bayer format. For inference also, the Bayer format images are converted to tri-color images which require sophisticated image processing pipeline. This conversion seems unnecessary given the proven ability of the deep networks to learn in multiple different scenarios. Moreover, the image processing pipeline can be simplified by directly using the Bayer images for computer vision tasks. Availability of the dataset for training the deep networks remains the challenge for building such systems; so, a methodology is needed to use the color images for training DNNs which can classify the Bayer images for a given application. The existing tri-color images from training datasets can be subsampled in pseudo-Bayer pattern for training such deep neural networks. This paper presents the methodology and results of one such method adopted for hand pose recognition application using SqueezeNet with encouraging performance. Using this methodology, we achieved 100% accuracy in nine out of ten hand postures with pseudo-Bayer images and in six out of ten hand postures with real Bayer images.