Age Range Estimation using MTCNN and VGG-Face Model
- URL: http://arxiv.org/abs/2104.08585v1
- Date: Sat, 17 Apr 2021 15:54:14 GMT
- Title: Age Range Estimation using MTCNN and VGG-Face Model
- Authors: Dipesh Gyawali, Prashanga Pokharel, Ashutosh Chauhan, Subodh Chandra
Shakya
- Abstract summary: Age range estimation using CNN is emerging due to its application in myriad of areas.
A deep CNN model is used for identification of people's age range in our proposed work.
- Score: 0.11454121287632513
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Convolutional Neural Network has amazed us with its usage on several
applications. Age range estimation using CNN is emerging due to its application
in myriad of areas which makes it a state-of-the-art area for research and
improve the estimation accuracy. A deep CNN model is used for identification of
people's age range in our proposed work. At first, we extracted only face
images from image dataset using MTCNN to remove unnecessary features other than
face from the image. Secondly, we used random crop technique for data
augmentation to improve the model performance. We have used the concept of
transfer learning in our research. A pretrained face recognition model i.e
VGG-Face is used to build our model for identification of age range whose
performance is evaluated on Adience Benchmark for confirming the efficacy of
our work. The performance in test set outperformed existing state-of-the-art by
substantial margins.
Related papers
- Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects [0.0]
Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs)
DenseNet201 had the greatest detection rate on the NEU dataset, falling in at 98.37 percent.
arXiv Detail & Related papers (2024-06-19T08:14:50Z) - Age Estimation Based on Graph Convolutional Networks and Multi-head
Attention Mechanisms [0.0]
Graph Convolutional Network (GCN) is used to extract features from irregular face images effectively.
This model can effectively improve the accuracy of age estimation and reduce the MAE error value to about 3.64, which is better than the effect of today's age estimation model.
arXiv Detail & Related papers (2023-10-12T06:26:39Z) - Identity-Preserving Aging of Face Images via Latent Diffusion Models [22.2699253042219]
We propose, train, and validate the use of latent text-to-image diffusion models for synthetically aging and de-aging face images.
Our models succeed with few-shot training, and have the added benefit of being controllable via intuitive textual prompting.
arXiv Detail & Related papers (2023-07-17T15:57:52Z) - MiVOLO: Multi-input Transformer for Age and Gender Estimation [0.0]
We present MiVOLO, a straightforward approach for age and gender estimation using the latest vision transformer.
Our method integrates both tasks into a unified dual input/output model.
We compare our model's age recognition performance with human-level accuracy and demonstrate that it significantly outperforms humans across a majority of age ranges.
arXiv Detail & Related papers (2023-07-10T14:58:10Z) - DeepDC: Deep Distance Correlation as a Perceptual Image Quality
Evaluator [53.57431705309919]
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models.
We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features.
We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
arXiv Detail & Related papers (2022-11-09T14:57:27Z) - Multi-layer Representation Learning for Robust OOD Image Classification [3.1372269816123994]
We argue that extracting features from a CNN's intermediate layers can assist in the model's final prediction.
Specifically, we adapt the Hypercolumns method to a ResNet-18 and find a significant increase in the model's accuracy, when evaluating on the NICO dataset.
arXiv Detail & Related papers (2022-07-27T17:46:06Z) - FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in
the Wild [50.8865921538953]
We propose a method to explicitly incorporate facial semantics into age estimation.
We design a face parsing-based network to learn semantic information at different scales.
We show that our method consistently outperforms all existing age estimation methods.
arXiv Detail & Related papers (2021-06-21T14:31:32Z) - Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for
Age and Gender Prediction on Mobile Ocular Images [53.913598771836924]
We address the use of selfie ocular images captured with smartphones to estimate age and gender.
We adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge.
Some networks are further pre-trained for face recognition, for which very large training databases are available.
arXiv Detail & Related papers (2021-03-31T01:48:29Z) - Image Restoration by Deep Projected GSURE [115.57142046076164]
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution.
We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
arXiv Detail & Related papers (2021-02-04T08:52:46Z) - Video-based Facial Expression Recognition using Graph Convolutional
Networks [57.980827038988735]
We introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based facial expression recognition.
We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0.
arXiv Detail & Related papers (2020-10-26T07:31:51Z) - The FaceChannel: A Fast & Furious Deep Neural Network for Facial
Expression Recognition [71.24825724518847]
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train.
We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks.
We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
arXiv Detail & Related papers (2020-09-15T09:25:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.