Recent studies have also focused on recognizing emotions from visual stimuli that encompass different objects. According to, the contribution in the field of emotion classification is mainly based on image, speech, video, and text stimuli to extract useful features for testing various kinds of recognition algorithms. With the rapid growth in digital media, emotion communication has increased through images, texts, speech, and audio-video contents. They are a vital source to perceive, convey, and express a particular feeling and can stimulate the human mind 3000 times more than the rational thoughts.

We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.Įmotions represent the mental and psychological state of a human being and are a crucial element of human daily living behavior. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions.

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways.

Assigning gender to inanimate objects