Tackling Age-Invariant Face Recognition with Non-Linear PLDA and Pairwise SVM

Face recognition approaches, especially those based on deep learning models, are becoming increasingly attractive for missing person identification, due to their effectiveness and the relative simplicity of obtaining information available for comparison. However, these methods still suffer from large accuracy drops when they have to tackle cross-age recognition, which is the most common condition to face in this specific task. To address these challenges, in this paper we investigate the contribution of different generative and discriminative models that extend the Probabilistic Linear Discriminant Analysis (PLDA) approach. These models aim at disentangling identity from other facial variations (including those due to age effects). As such, they can improve the age invariance characteristics of state-of-the-art deep facial embeddings. In this work, we experiment with a standard PLDA, a non-linear version of PLDA, the Pairwise Support Vector Machine (PSVM), and introduce a nonlinear version of PSVM (NL--PSVM) as a novelty. We thoroughly analyze the proposed models' performance when addressing cross-age recognition in a large and challenging experimental dataset containing around 2.5 million images of 790,000 individuals. Results on this testbed confirm the challenges in age invariant face recognition, showing significant differences in the effects of aging across embedding models, genders, age ranges, and age gaps. Our experiments show as well the effectiveness of both PLDA and its proposed extensions in reducing the age sensitivity of the facial features, especially when there are significant age differences (more than ten years) between the compared images or when age-related facial changes are more pronounced, such as during the transition from childhood to adolescence or from adolescence to adulthood. Further experiments on three standard cross-age benchmarks (MORPH2, CACD-VS and FG-NET) confirm the proposed models' effectiveness

Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment

In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely

Verifikasi Wajah Menggunakan Deep Metric Learning pada Data Wajah dengan Disparitas Umur yang Besar

Penuaan wajah merupakan proses biologi yang kompleks yang ditandai dengan adanya perubahan fisik yang terjadi pada wajah, seperti perubahan warna kulit, adanya bercak hitam, timbulnya kerutan dan sebagainya seiring bertambahnya usia. Penuaan wajah menjadi salah satu faktor penting dalam proses pengenalan wajah atau biasa disebut dengan istilah face recognition. Untuk mengatasi hal ini, dibutuhkan suatu metode pengenalan atau verifikasi wajah yang dapat mengenali individu yang sama dengan perbedaan usia. Di tugas akhir ini digunakan sebuah metode bernama Deep Metric Learning yang menggabungkan Deep Learning dan Metric Learning. Sistem dibangun dengan menggunakan arsitektur Siamese Neural Network yang mengandung dua masukkan citra. Kedua citra ini terdiri dari foto wajah seseorang ketika masih muda (kanak-aknak atau remaja) dan dewasa. Sistem akan melakukan serangkaian proses verifikasi. Pada bagian akhir, kedua citra diprediksi apakah berasal dari individu yang sama atau tidak. Luaran dari sistem ini adalah skor kemiripan (similarity score) yang nantinya ditentukan batasan (threshold) terlebih dahulu agar bisa mendapatkan hasil akhir sebenarnya. Hasil prediksi direpresentasikan sebagai nilai biner, yaitu 0 bernilai salah dan 1 bernilai benar