No abstract

No abstract

No abstract

EVWUDFW²7KH ZRUN SURSRVHG LQ WKLV SDSHU LV DQ DWWHPSW WR GHYHORS WZR UHFRJQL]HUV IRU 2GLD KDQGZULWLQJ EDVHG RQ EDVLF WUDQVIRUPDWLRQ VFKHPHV DQG FRPSDUHV WKHLU SURV DQG FRQV %RWK WKH UHFRJQL]HUV SXW HPSKDVLV RQ H[SORLWLQJ WKH LQKHUHQW FKDUDFWHU LVWLFV RI WKH 2GLD QXPHUDO LPDJHV 7KH SURSRVHG PHWKRG DQDO\]HV WKH XVH RI 'LVFUHWH &RVLQH 7UDQVIRUPDWLRQ '&7 DQG 'LVFUHWH :DYHOHW 7UDQVIRUPDWLRQ ':7 IRU WKLV SXUSRVH 5HFRJQLWLRQ E\ FODVVL¿FDWLRQ LV DFKLHYHG E\ IHHGLQJ WKHVH YHFWRUV DV LQSXW WR D %DFN 3URSDJDWLRQ 1HXUDO 1HWZRUN %311 5HFRJQLWLRQ UHVXOWV DUH REWDLQHG DQG FRPSDUHG E\ H[SHULPHQWDOO\ YDU\LQJ WKH FODVVL¿HU SDUDPHWHUV 2XU H[SHULPHQWDO UHVXOWV FRPH RXW WR EH SURPLVLQJ 7KXV ¿QDOO\ ZH FRPH RI ZLWK D UREXVW UHFRJQL]HU IRU KDQGZULWWHQ 2GLD QXPHUDOV .H\ZRUGV²2GLD 2&5 1XPHUDO UHFRJQLWLRQ '&7 ':7 %311 6&*

No abstract

In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters. These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG features (HOG-BOW). These feature extraction algorithms are compared to other well-known techniques: principal component analysis, the discrete cosine transform, and the direct use of pixel intensities. The extracted features are given to three different types of support vector machines for classification, namely a linear SVM, an SVM with the RBF kernel, and a linear SVM using L2-regularization. We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets: Bangla, Odia and MNIST. The results show that the HOG-BOW, BOW and HOG method significantly outperform the other methods. The HOG-BOW method performs best with the L2-regularized SVM and obtains very high recognition accuracies on all three datasets.

No abstract

Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work.