Emotion Detection from Image Using Deep Face

Abstract

Emotion detection from facial expressions is a vital component of affective computing, enabling intelligent systems to interpret human affective states and respond appropriately. This study explores a computer vision–based approach using Deep Face, a deep learning framework for face analysis, to automatically detect emotions from images. A benchmark dataset of facial expressions is used for training and evaluation. Deep Face employs deep convolutional neural networks (CNNs) to extract high-level facial features and map them into a compact embedding space. These embeddings are classified into seven basic emotions: happiness, sadness, anger, fear, surprise, disgust, and neutral. The models are evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Intelligent systems that can be applied in healthcare, e-learning, security, and human–computer interaction.

Goal: Develop an image-based system for detecting human emotions from facial expressions.

Dataset: FER-2013 and CK+ datasets containing labeled facial expression images.

Models Evaluated: Deep Face (CNN-based), VGG-Face, Res Net-based embeddings.

Best Model: Deep Face achieved the highest accuracy of 93.4% on FER-2013.

Applications: Smart classrooms, mental health monitoring, driver safety, surveillance, and customer behavior analysis.

Future Work: Real time video emotion detection, multimodal affect recognition, and handling occlusions.