Nonverbal Communication through Facial Expression in Diverse Conditions

In this chapter, we investigated computer vision technique for facial expression recognition, which increase both - the recognition rate and computational efficiency. Local and global appearance-based features are combined in order to incorporate precise local texture and global shapes. We proposed Multi-Level Haar (MLH) feature based system, which is simple and fast in computation. The driving factors behind using the Haar were its two interesting properties - signal compression and energy preservation. To depict the importance of facial geometry, we first segmented the facial components like eyebrows, eye, and mouth, and then applied feature extraction on these facial components only. Experiments are conducted on three well known publicly available expression datasets CK, JAFFE, TFEID and in-house WESFED dataset. The performance is measured against various template matching and machine learning classifiers. We achieved highest recognition rate for proposed operator with Discriminant Analysis Classifier. We studied the performance of proposed approach in several scenarios like expression recognition from low resolution, recognition from small training sample space, recognition in the presence of noise and so forth

A Review of Movie Recommendation System : Limitations, Survey and Challenges

Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored

Comparative Analysis between DCT & DWT Techniques of Image Compression

Image compression is a method through which we can reduce the storage space of images, videos which will helpful to increase storage and transmission process's performance. In image compression, we do not only concentrate on reducing size but also concentrate on doing it without losing quality and information of image. In this paper, two image compression techniques are simulated. The first technique is based on Discrete Cosine Transform (DCT) and the second one is based on Discrete Wavelet Transform (DWT). The results of simulation are shown and compared different quality parameters of its by applying on various images Keywords: DCT, DWT, Image compression, Image processin

Recognition of Facial Expressions using Local Mean Binary Pattern

In this paper, we propose a novel appearance based local feature extraction technique called Local Mean Binary Pattern (LMBP), which efficiently encodes the local texture and global shape of the face. LMBP code is produced by weighting the thresholded neighbor intensity values with respect to mean of 3 x 3 patch. LMBP produces highly discriminative code compared to other state of the art methods. The micro pattern is derived using the mean of the patch, and hence it is robust against illumination and noise variations. An image is divided into M x N regions and feature descriptor is derived by concatenating LMBP distribution of each region. We also propose a novel template matching strategy called Histogram Normalized Absolute Difference (HNAD) for comparing LMBP histograms. Rigorous experiments prove the effectiveness and robustness of LMBP operator. Experiments also prove the superiority of HNAD measure over well-known template matching methods such as L2 norm and Chi-Square measure. We also investigated LMBP for facial expression recognition low resolution. The performance of the proposed approach is tested on well-known datasets CK, JAFFE, and TFEID

PCA Based Handwritten Character Recognition System Using Support Vector Machine & Neural Network

Pattern recognition deals with categorization of input data into one of the given classes based on extraction of features. Handwritten Character Recognition (HCR) is one of the well-known applications of pattern recognition. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a Principal Component Analysis (PCA) based feature extraction method is investigated for developing HCR system. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. These method have been used as features of the character image, which have been later on used for training and testing with Neural Network (NN) and Support Vector Machine (SVM) classifiers. HCR is also implemented with PCA and Euclidean distanc

Robust content-based image retrieval using ICCV, GLCM, and DWT-MSLBP descriptors

Content-based image retrieval (CBIR) retrieves visually similar images from a dataset based on a specified query. A CBIR system measures the similarities between a query and the image contents in a dataset and ranks the dataset images. This work presents a novel framework for retrieving similar images based on color and texture features. We have computed color features with an improved color coherence vector (ICCV) and texture features with a gray-level co-occurrence matrix (GLCM) along with DWT-MSLBP (which is derived from applying a modified multi-scale local binary pattern [MS-LBP] over a discrete wavelet transform [DWT], resulting in powerful textural features). The optimal features are computed with the help of principal component analysis (PCA) and linear discriminant analysis (LDA). The proposed work uses a variancebased approach for choosing the number of principal components/eigenvectors in PCA. PCA with a 99.99% variance preserves healthy features, and LDA selects robust ones from the set of features. The proposed method was tested on four benchmark datasets with Euclidean and city-block distances. The proposed method outshines all of the identified state-of-the-art literature methods

A Review of Movie Recommendation System : Limitations, Survey and Challenges

Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored

Adapting face recognition to the masked world: leveraging deep attention networks with a custom dataset

In the era of COVID-19, past face recognition algorithms' performance is downgraded due to the partial occlusion of face mask. A new Indian face image dataset has been proposed titled, Handcrafted Indian Face (HIF) dataset, addressing the issues, viz. variegated illumination, pose, and partial occlusion conditions. It bridges the gap between the performance of the DL models used, pre and post COVID-19 effect. A novel idea for choosing the train-test sample has been presented in the paper, which improves the accuracy on existing state of art DL models. In this paper, a new DL architecture has been proposed named the InceptBlock Enhanced Attention Fusion Network (IBEAFNet) which consists of the combination of ECBAM (Enhanced Convolution Block Attention Module) and InceptionV3 architecture. The proposed architecture's attention layer placement allows it to suppress less relevant mask regions of face, while emphasizing on significant fine and coarse level features with reduced complexity. IBEAFNet is trained and tested on two existing datasets, viz. Casia & Yale (including simulated masked images) and the proposed HIF dataset. The performance of IBEAFNet is compared with the results fetched by changing the attention layers in IBEAFNet with the blocks of SENet and CBAM. IBEAFNet outperformed the state-of-art models with the accuracy of 91.00%, 89.5%, and 93.00% on CASIA, Yale, and HIF dataset, respectively.</p