Multi-scale feature fusion model followed by residual network for generation of face aging and de-aging

Atkale, Dipali Vasant; Pawar, Meenakshi M.; Deshpande, Shabdali C.; Yadav, Dhanashree M.

doi:10.1007/s11760-021-02015-z

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…We used the UTKFace database [24] [2] [25] to evaluate the proposed method. UTKFace have a large number of face image where subjects are of a wide range of ages (from 0 to 116 years).…”

Section: Suppositionsmentioning

confidence: 99%

Human face aging based on active appearance model using proper feature set

Khajavi

Ahmadyfard²

2022

SIViP

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In this paper, a simulation of the human face evolution from youth to old age is presented. The appearance of facial changes due to aging depends on various factors such as genetics, ethnicity, and lifestyle. However, the human faces in an age group have similar characteristics. These characteristics can be used to estimate the image of a person's face in the coming years. In this work, the goal is to maintain a person's appearance and accurately estimate the face at the target age; in such a way that the simulated face should be realistic and have the appearance of the texture characteristics of the target age. To achieve this goal, the number of feature points and their arrangement on the face is very important. First, we suggest proper feature points. Then, using the Active Appearance Model (AAM) method and the presented model, templates are obtained as representative of different age groups. To maintain the unique geometric characteristics of the input face and apply these characteristics to the target age template, the proposed feature point pattern and the MLS method have been used. Then, using the active appearance model method and having the target age template containing the geometric characteristics of the input face, the steps of changing the input face age to reach the target age are presented. Finally, the results of the survey, using two methods of age recognition and real image recognition (separately for man/woman images), show an average of 80.77% (male images) and 81.36% (female images) correct answers of the participants in this Polls.

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“…We used the UTKFace database [24] [2] [25] to evaluate the proposed method. UTKFace have a large number of face image where subjects are of a wide range of ages (from 0 to 116 years).…”

Section: Suppositionsmentioning

confidence: 99%

Human face aging based on active appearance model using proper feature set

Khajavi

Ahmadyfard²

2022

SIViP

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“…De-aging networks, a form of deep learning, generate realistic images with a younger appearance, aiding forensic investigations by enabling accurate face recognition from old sketch comparisons (Rafique et al, 2021;Atkale et al, 2022). For instance, if a crime suspect is identified only through an aged sketch, de-aging networks can produce a younger version for comparison, improving the chances of identification.…”

Section: Introductionmentioning

confidence: 99%

Precision sketching with de-aging networks in forensics

Martis,

Sannidhan,

Pratheeksha Hegde

et al. 2024

Front. Signal Process.

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Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The de-aging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system’s superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for age-invariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications.

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“…Aloraini [41] proposed a novel approach based on fusing three streams of convolutional neural networks. Atkale et al [46] designed an approach known as the multi-scale feature fusion model followed by a residual network.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Erasing Stripe Pyramid Network for Face Forgery Detection

Duan

Zhang

et al. 2023

Preprint

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ace forgery detection aims to distinguish between real and fake facial images or videos by identifying manipulated or forged visual media. The main challenge in face forgery detection is achieving high model generalization ability, i.e., satisfactory performance under cross-database scenarios where the training and testing datasets are from different forgery methods. To achieve this goal, this paper presents an Attention-Erasing Stripe Pyramid Network (ASPNet) to utilize high-frequency noises and exploit both the RGB and fine-grained frequency clues. First, since separately extracting features from different scales and granularities will ignore their complementarity, we employ a Stripe Pyramid Block (SPB) to learn multi-scale and multi-granularity features simultaneously. Second, to make the model to focus on useful information and suppress noise, a Two-Stage Attention Block (TSAB) is introduced by combining spatial attention and channel attention to filter out the pixel-wise and channel-wise noise in the learned feature maps. Finally, to dynamically guide the model to pay attention to different areas of the human face, an Attention Erasing (AE) scheme is adopted by randomly erasing units in attention maps. Sufficient experiments demonstrate that ASPNet has superior performance than F3-Net on the FaceForensics++ dataset. The Area Under the Receiver Operating Characteristic Curve (AUC) and the Accuracy (ACC) of our model reach 77.4% and 70.85% respectively, whichare improved by 0.83% and 1.28% compared with F3-Net. Our code is available at: https://github.com/NWPU-Zwu.

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Multi-scale feature fusion model followed by residual network for generation of face aging and de-aging

Cited by 8 publications

References 24 publications

Human face aging based on active appearance model using proper feature set

Human face aging based on active appearance model using proper feature set

Precision sketching with de-aging networks in forensics

An Attention-Erasing Stripe Pyramid Network for Face Forgery Detection

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