#FACE MORPH AGE PROGRESSION APPLICATIONS VERIFICATION#The proposed method has better performance and higher accuracy than current state-of-the-art methods.įACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NETWORK Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. Accordingly, there is a demand to develop robust methods to verify facial images when they age. Facial aging is a texture and shape variations that affect the human face as time progresses. #FACE MORPH AGE PROGRESSION APPLICATIONS PATCH#Our new objective function, as well as multi-scale patch discrimination and, has shown both qualitative and quantitative improvements over the state-of-the-art approaches in terms of face verification, rank-1 identification, and age estimation on benchmarked children datasets.This paper proposes a deep learning method for facial verification of aging subjects. Thus, we present coarse-to-fine Self-Attention Multi-Scale Patch generative adver-sarial nets (SAMSP-GAN) model. Moreover, we also introduce Self-Attention Block (SAB) to learn global and long-term dependencies within an internal representation of a child's face. To facilitate child age synthesis, we apply a multi-scale patch discriminator learning strategy for training conditional generative adversarial nets (cGAN) which increases the stability of the discriminator, thereby making the learning task progressively more difficult for the generator. In this work, we propose a child face age-progress and regress framework that generates photo-realistic face images with preserved identity. state-of-the-art frameworks mostly focus on adult or long-span aging. The two primary necessities of face age progression and regression, are identity preservation and aging exactitude. We demonstrate that Adam works well in practice whenĮxperimentally compared to other stochastic optimization methods.įace age progression and regression have accumulated significant dynamic research enthusiasm because of its gigantic effect on a wide scope of handy applications including finding lost/wanted persons, cross-age face recognition, amusement, and cosmetic studies. That is comparable to the best known results under the online convex Properties of the algorithm and provide a regret bound on the convergence rate We also analyze the theoretical convergence Some connections to related algorithms, on which Adam The hyper-parameters have intuitive interpretations and typically Rescaling of the gradients by adapting to the geometry of the objectiveįunction. The method exhibits invariance to diagonal Method is also ap- propriate for non-stationary objectives and problems with Suited for problems that are large in terms of data and/or parameters. Method is computationally efficient, has little memory requirements and is well The method is straightforward to implementĪnd is based an adaptive estimates of lower-order moments of the gradients. We introduce Adam, an algorithm for first-order gradient-based optimization Experimental results show that the proposed method can achieve state-of-the-art performance on both our dataset as well as the other widely used dataset for face recognition across age, MORPH dataset. To the best of our knowledge, it is by far the largest publicly available cross-age face dataset. The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62. To thoroughly evaluate our work, we introduce a new large-scale dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD). In the testing phase, the proposed method only requires a linear projection to encode the feature and therefore it is highly scalable. By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC is able to encode the low-level feature of a face image with an age-invariant reference space. We propose a novel coding framework called Cross-Age Reference Coding (CARC). Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. However, face recognition and retrieval across age is still challenging. Recently, promising results have been shown on face recognition researches.
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