Chapters authored
Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature
Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. At last, we validate performance of our work using seven performance metrics that are Accuracy, Recall, Rank-1 Score, Precision, F-Score, Recognition rate and computation time.
Part of the book: Artificial Intelligence Annual Volume 2022