Visible to the public Investigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence

TitleInvestigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence
Publication TypeConference Paper
Year of Publication2020
AuthorsSarathy, N., Alsawwaf, M., Chaczko, Z.
Conference Name2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY)
Keywordsartificial intelligence, biometric identification research, biometrics, biometrics (access control), DNN, Ear, explainable artificial intelligence, explanation, face recognition, faces, facial feature extraction, Facial features, feature extraction, feature set diagnosis, feature vector, feature vectors, geometric ratio expressions, Geometric Ratios, human face identification, human face profile identification, Human Profile Recognition, Identification, image segmentation, Nose, Pixel Segmentation, pubcrawl, Resiliency, Scalability, xai
AbstractHuman identification is a well-researched topic that keeps evolving. Advancement in technology has made it easy to train models or use ones that have been already created to detect several features of the human face. When it comes to identifying a human face from the side, there are many opportunities to advance the biometric identification research further. This paper investigates the human face identification based on their side profile by extracting the facial features and diagnosing the feature sets with geometric ratio expressions. These geometric ratio expressions are computed into feature vectors. The last stage involves the use of weighted means to measure similarity. This research addresses the problem of using an eXplainable Artificial Intelligence (XAI) approach. Findings from this research, based on a small data-set, conclude that the used approach offers encouraging results. Further investigation could have a significant impact on how face profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89.
Citation Keysarathy_investigation_2020