# Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier
## 2 Related Works  
Before the deep learning approach became popular, the hand-crafted approach was the way to go for image recognition, particularly vision-based. A hand-crafted approach often consists of several sections of image pre-processing and specific crafted feature extractions modules. [PERSON] (2017) [1] proposed a hand gesture recognition using the shape and texture evidence in complex backgrounds. The National University of Singapore (NUS) Hand Posture dataset used was subjected to segmentation and morphological operation for image pre-processing. The pre-processing mainly targeted the internal noises prior to using the Gabor filter to retrieve the texture features of the images. A differentiable intensity profile was created through the Gabor filter and smoothened through the Gaussian filter where the intensity information then fed into the classifier. [PERSON] et al. (2018) [2] proposed an implementation of hand posture recognition using several descriptors on three different databases, namely American Sign Language (ASL), Arabic Sign Language (ArSL) and the NUS Hand Posture dataset. The system architecture was categorized into three phases, namely hand detection, feature extraction and classification. Several descriptors such as Hu's Moment Descriptor (Hu's MD), Zernike Moments Descriptor (ZMD), Generic Fourier Descriptor (GFD) and Local Binary Pattern Descriptor (LBPD) were used to detect the hand posture region. In Hu's MD, the moment invariants were computed based on the information provided by both the external shape and internal edges. While for LBPD, the image was divided into several non-overlapping blocks; LBP histograms were then computed for each individual block. Finally, the local binary pattern (LBP) histograms were concatenated into a single vector. As for ZMD, a statistical measure of pixel distribution around the centre of gravity of the shape was used to detect the hand in the image and constructed a bounding box around it to eliminate the unwanted surrounding background. [PERSON] et al. (2018) [3] proposed a hand gesture recognition system based on the Histogram of Oriented Gradients (HOG) and LBP using the NUS dataset. The architecture of the proposal algorithm worked in a manner wherein feature extraction was performed separately and paralleled via HOG and LBP followed by fusing the collected features into one, and then followed by the Support Vector Machine (SVM) for classification. HOG features were used to acquire the edge and local shape information, while LBP features were used to extract the texture features which were robust to the grey level transform, therefore, as rotational change. In the final stage, SVM with the radial basis function (RBF) was used to classify the feature obtained. [PERSON] and [PERSON] (2019) [4] proposed a hand gesture recognition using SVM with the chain code histogram (CCH) used for feature extraction on the NUS dataset. The process began with a thresholding process as part of the pre-processing to produce binary images of hand posture for feature extraction. Ridler and Calvard thresholding (RCT) was used to segment the region of interest. RCT thresholding worked by considering the average value of the intensity pixels as an initial threshold and the foreground and background classes were first separated based on the computed average foreground mean and background mean. For feature vector extraction, CCH segregated the binary image according to cluster-based thresholds into grid blocks and the histogram was then calculated based on the frequently occurring discrete values. In order to obtain good classification, feature extraction has become a much more crucial task, especially with complex or noisy backgrounds. Researchers had then started to adopt more of a deep learning approach to ease the feature extraction module creation. [PERSON] et al. (2017) [5] proposed a static hand gesture recognition model with parallel CNNs for space human-robot interaction on the ASL dataset.