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Holoscopic micro-gesture recognition (HoMG) database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes).


Holoscopic micro-gesture recognition (HoMG) database consists of 3 hand gestures: Button, Dial and Slider from 40 subjects with various ages and settings, which includes the right and left hand, two of record distance.

For video subset: There are 40 subjects, and each subject has 24 videos due to the different setting and three gestures. For each video, the frame rate is 25 frames per second and length of videos are from few seconds to 20 seconds and not equally. The whole dataset was divided into 3 parts. 20 subjects for the training set, 10 subjects for development set and another 10 subjects for testing set.

For image subset: Video can capture the motion information of the micro-gesture and it is a good way for micro-gesture recognition. From each video recording, the different number of frames were selected as the still micro-gesture images. The image resolution 1920 by 1080. In total, there are 30635 images selected. The whole dataset was split into three partitions: A Training, Development, and Testing partition. There are 15237 images in the training subsets of 20 participants with 8364 in close distance and 6853 in the far distance. There are 6956 images in the development subsets of 10 participants with 3077 in close distance and 3879 in far distance. There are 8442 images in the testing subsets of 10 participants with 3930 in close distance and 4512 in far distance.


 food recognition  



The data consists of 222430 training and 55096 testing images belonging to 2 classes. For the preparation of this dataset, we used images from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset.

Please only cite our work, for Food/Non-Food detection, in case of classification problems on the individual datasets, please cite and use them.