Understanding the different categories of facial expressions is crucial to comprehend human cognition as well as for designing these computational interfaces. Deep learning is an exciting area in machine learning and has seen an exponential rise in recent years in various applications. In this paper, we use deep learning networks to identify facial expressions in low resolution images. The major contribution of this paper is in analysing the effect of spatial resolution reduction on the facial expression recognition rates. There is not much work reported in this area. Detailed analysis to establish a relationship between recognition rates and drop in spatial resolution is carried out. We have successfully demonstrated that even with extreme spatial resolution reduction in full face images accurate facial expression recognition can be achieved using the proposed frameworks, and there is negligible effect on recognition rates. In addition to full face images, we have also successfully demonstrated that the proposed networks work well for various segments of the face. The results obtained stay consistent even for extreme reduction in spatial resolution of these facial segments. In order to evaluate performance of the proposed frameworks, rigorous sets of experiments are presented in the paper. We can conclude that even when human computer interface systems have access to entire fontal face images, only a small segment of the image is required for accurate facial expression recognition, and the results obtained are consistent even under extreme spatial resolution reductions.

Due to the complexity of the condition and the lengthy diagnostic process, skin diseases are a widespread health issue. In addition to having an impact on one's health, skin conditions can also have a negative psychological and social impact if not identified and treated at an early stage. Most of them are brought on by infections or by different environmental factors. Eczema, psoriasis, melanoma, acne cherry, vitiligo, basal cell carcinoma, squamous cell carcinoma, melanocytic nevi, seborrheic keratosis, and acrochordon (skin tags) are a few examples of distinct skin diseases. However, the expense of such a diagnostic is still quite high. The advancement of image processing methods and machine learning has enabled a swift and precise diagnosis, contributing to the early identification of skin conditions. Skin-related problems affect millions of people worldwide. This research gives a literature review on the many methods used to identify skin diseases. In this study, we scrutinized six papers that presented various methods for skin disease identification. These approaches encompassed techniques such as extracting features utilizing the gray level co-occurrence matrix (GLCM) and employing a multi-layer perceptron classifier (MLP) to differentiate between melanocytic nevi and malignant melanoma, among others. The opportunities for improving the diagnosis and treatment of skin ailments, assisting in the earlier detection of skin cancer, and improving patient outcomes differ based on the suggested process.

One of the important components of an intelligent Human computer Interface system is accurate classification of the various affect states. Such interface systems are however plagued by a recurring problem of image occlusion. The challenge hence is to be able to classify the various affect states accurately from whatever portions of the face are available to the system. This paper attempts to investigate if there are segments within the facial region which carry sufficient information about the affect states. In this paper we have used two pre-defined Convolutional Neural networks (CNN). We have implemented a ResNet-50 network and a modified version of ResNet-50 which has a Squeeze and Excitation network connected to ResNet-50. This is called SE-ResNet-50. We use these two networks to classify seven basic affect states of Angry, Contempt, Disgust, Fear, Happy, Sad and Surprise from various segments of the face. We partition the face into four regions with each region comprising of only 50% of the original data. The results obtained are compared with that obtained using the full face. The validation accuracy values are obtained for full face as well as the four segments of the face. The paper also calculates precision and recall for each partitioned area for each of the affect states using the two networks. Our evaluation shows that both, ResNet-50 as well as SE-ResNet-50 are successful in accurately classifying all the 7 affect state from the Right segment, Left segment Lower segment and Upper segment of the face. While ResNet-50 performs marginally better compared to the SE-ResNet-50 in identifying the various affect states form the right, left and lower segments of the face, SE-ResNet-50 performs better in identifying the affect states from the upper segment of the face. We can thus conclude that right segment, left segment, lower segment and upper segments of the face contain sufficient information to correctly classify the seven affect states. The experimental results presented in this paper show that pre-defined Convolutional Neural Networks gives us very high accuracy, precision and recall values and hence can be used to accurately classify affect states even when there are occlusions present in the image and only certain portions of the face are available for analysis.

Affect detection is a key component in developing Intelligent Human Computer Interface (IHCI) systems. State of the art affect detection systems assume the availability of full un-occluded face images. However image occlusion is a prominent problem which one comes across while dealing with such systems. The challenge is to identify affect states from portions of the face that are available. This paper proposes a novel method of assessing only a segment of the face instead of the whole face for affect detection. This paper aims at finding segments of the face which contain sufficient information to correctly classify the basic affect states. This work uses Convolutional Neural Networks (CNN) with transfer learning to detect 7 basic affect states viz. Angry, Contempt, Disgust, Fear, Happy, Sad and Surprise from a few prominent facial segments. Full face images are partitioned into separate segments viz. Right segment, Left segment, Lower segment and Upper segment. Modified VGG-16 and ResNet-50 networks were trained using each of the segments. Experiments were conducted using these facial segments and results obtained were compared with that of the full face. Using the VGG-16 network, we have been able to achieve validation accuracies of 96.8% for Full face, 97.3% for Right segment of the face, 97.3% for Left segment of the face, 96.6% for Lower segment of the face and 84.7% for Upper segment of the face. The validation accuracies are higher using the ResNet-50 network. Using the ResNet-50 network we have been able to achieve validation accuracies of 99.7% for Full face, 99.47% for Right segment of the face, 100% for Left segment of the face, 99.6% for Lower segment of the face and 90.8% for Upper segment of the face. Apart from accuracy, the other performance matrices used in this work are Precision, Recall and f1-score. Our evaluation, based on these performance matrices show that the results obtained for Right segment, Left segment and Lower segment of the face using both, VGG-16 as well as ResNet-50 networks, are comparable with that of the Full face. Experiments performed clearly indicate that Right segment, Left segment and Lower segment of the face contain sufficient information about the seven affect states and that CNN with transfer learning can be used to accurately classify them.

Affect detection is a key component in developing intelligent human computer interface systems. State-of-the-art affect detection systems assume the availability of full un-occluded face images. This work uses convolutional neural networks with transfer learning to detect 7 basic affect states, viz. Angry, Contempt, Disgust, Fear, Happy and Sad. The paper compares three pre-trained networks, viz. VGG16, ResNet50 and a SE-ResNet50, in which a new architectural block of squeeze and excitation has been integrated with ResNet50. Modified VGG-16, ResNet50 and SE-ResNet50 networks are trained on images from the dataset, and the results are compared. We have been able to achieve validation accuracies of 96.8%, 99.47%, and 97.34% for VGG16, ResNet50 and SE-ResNet50, respectively. Apart from accuracy, the other performance matrices used in this work are precision and recall. Our evaluation, based on these performance matrices, shows that accurate affect detection is obtained from all the three networks with Resnet50 being the most accurate.

Detection of learning centered affect is an important element in developing intelligent educational interfaces that are capable of responding to the affective needs of students. There exists an inseparable link between emotions and cognition and the field of Affective Computing (AC) aspires to narrow the communicative gap between human beings and computers. This is achieved by developing computational systems that recognize and respond to the affective states of the user. Learning centered affective states include Boredom, Confusion, Delight, Engagement and Frustration. This paper uses the Local binary patterns (LBP) ans a feature extraction algorithm and PCA for dimentionality reduction to detect confusion. This algorithm is applied to the full face as well as Lip and eye sections of the face. The classifier that is used is the chi-square classifier. The template matching achieved the generalization performance of 81.5% for 2-class task and 76.2 % for the 4-class on the full face. Template matching on the Lip patch achieved the generalization performance of 79 % for 2-class task which involved Confused and not confused The results obtained suggest that instead of working with the entire face, we could work only with the lip portion to identify the affect states as it would computationally be more efficient.

Computer systems which are capable of emotion detection via automated devices open up new horizons in Human-Computer Interaction (HCI). Due to the inseparable link between emotions and cognition, the field of Affective Computing aspires to narrow the communicative gap between human beings and computers. This is achieved by developing systems that not only recognize but also respond to the affective states of the user. This paper presents a literature survey on the various Affect detection techniques including Facial expression analysis, Speech analysis, Multi-modality and Gaze detection. The aim of the paper is to obtain a deeper understanding of affective states, which will form the foundation for designing a data-driven Human Computer interface.

Iris recognition as a biometric technique for personal identification and verification is examined. The motivation for this stems from the observation that the human iris provides a unique structure suitable for non-invasive biometric assessment. In particular the irises are as distinct as fingerprints or patterns of retinal blood vessels and the appearance of the iris is amenable to remote examination. In this paper we have used a database of iris images of more than 100 people, which was used in the implementation of the iris recognition software. The software developed uses a novel technique of localization, alignment, pattern matching of the irises and finally the decision regarding the degree of match.