  Facial emotional recognition is one of the essential tools used by
recognition psychology to diagnose patients. Face and facial emotional
recognition are areas where machine learning is excelling. Facial Emotion
Recognition in an unconstrained environment is an open challenge for digital
image processing due to different environments, such as lighting conditions,
pose variation, yaw motion, and occlusions. Deep learning approaches have shown
significant improvements in image recognition. However, accuracy and time still
need improvements. This research aims to improve facial emotion recognition
accuracy during the training session and reduce processing time using a
modified Convolution Neural Network Enhanced with Extreme Learning Machine
(CNNEELM). The system entails (CNNEELM) improving the accuracy in image
registration during the training session. Furthermore, the system recognizes
six facial emotions happy, sad, disgust, fear, surprise, and neutral with the
proposed CNNEELM model. The study shows that the overall facial emotion
recognition accuracy is improved by 2% than the state of art solutions with a
modified Stochastic Gradient Descent (SGD) technique. With the Extreme Learning
Machine (ELM) classifier, the processing time is brought down to 65ms from
113ms, which can smoothly classify each frame from a video clip at 20fps. With
the pre-trained InceptionV3 model, the proposed CNNEELM model is trained with
JAFFE, CK+, and FER2013 expression datasets. The simulation results show
significant improvements in accuracy and processing time, making the model
suitable for the video analysis process. Besides, the study solves the issue of
the large processing time required to process the facial images.
