Techniques for facial age progression and regression have many applications and a myriad of challenges. Our model also demonstrates a 22.4% gain in identity preservation measured by a facial recognition neural network. Quantitatively, our model exhibits an overall gain of 77.0% (male) and 13.8% (female) in gender fidelity measured by a gender classifier for the simulated photos across the age spectrum. Compared to the CAAE approach, our new model demonstrates noticeable visual improvements. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of aging on 1,156 male and 1,207 female infant and toddler face photos. In our approach, we extend the CAAE architecture to 1) incorporate gender information, and 2) augment the model's overall architecture with an identity-preserving component based on facial features. We propose a new deep learning method inspired by the successful Conditional Adversarial Autoencoder (CAAE, 2017) model. In particular, the lack of visually detectable gender characteristics and the drastic appearance changes in early life contribute to the difficulty of the task.
Nevertheless, it remains a challenging task to generate accurate age-progressed faces from infant or toddler photos. In recent years, deep learning-based approaches have made remarkable progress in modeling the aging process of the human face. Realistic age-progressed photos provide invaluable biometric information in a wide range of applications. The review is completed by discussing the results obtained on public datasets, so as the impact of different aspects on system performance, together with still open issues. This paper provides an analysis of the deep methods these are analysed from different points of view: the network architecture together with the learning procedure, the used datasets, data preprocessing and augmentation, and the exploitation of additional data coming from gender, race and face expression. The exciting results obtained have been recently surveyed on almost all the specific face analysis problems the only exception stands for age estimation, whose last survey dates back to 2010 and does not include any deep learning based approach to the problem. The explosion of the deep learning paradigm, that is determining a spectacular increasing of the performance, is in the public eye consequently, the number of approaches based on deep learning is impressively growing and this also happened for age estimation. With the proposed PAGAN, the face recognition accuracy with synthesized images has increased 0.21% and the image quality rating has increased around 5%, which proves the effectiveness and validity of proposed method.įace analysis includes a variety of specific problems as face detection, person identification, gender and ethnicity recognition in the last two decades, significant research efforts have been devoted to the challenging task of age estimation from faces, as witnessed by the high number of published papers. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods not only in the accuracy of age classification but also in the image quality. The proposed face aging framework with PAGAN is a combination of age estimation, identity preservation, and image de-noising. Face images are featured by age, identity, and fine-grained pixel-value to ensure the quality, which is a typical multi-task learning problem. To meet this challenge, we propose a face aging framework named as Pixel-level Alignment GAN, PAGAN, to synthesize faces of different age groups. However, there is still a huge gap between the synthesized face image and the real face in terms of quality and consistency due to identity ambiguity and image distortion caused by existing face aging methods.
#FACE MORPH AGE PROGRESSION SOFTWARE VERIFICATION#
Face aging is of great significance in cross-time identity verification problem.