The training process has been tested on NVIDIA TITAN X (12GB). The training time for 50 epochs on UTKFace (23,708 images in the size of 128x128x3) is about two and a half hours. In this paper, the age network (AgeNet) in is used to extract the face age descriptor and then the age estimation is carried out by using the divide-and-rule strategy. The AgeNet uses an approach based on regression and classification to construct an age-estimated deep CNN. When you're constantly multitasking and putting in long days-athe right apps can be a lifesaver. I propose you hit on the combination that works for you regadless the age, your personal and professional lives run more effectively. The automatic age-progression software can run on a standard computer and takes about 30 seconds to generate results for one face.

Facial Age Progression Software
Facial Age Regression Software

TensorFlow implementation of the algorithm in the paper Age Progression/Regression by Conditional Adversarial Autoencoder.

Thanks to the Pytorch implementation by Mattan Serry, Hila Balahsan, and Dor Alt.

Pre-requisites

Python 2.7x
Scipy 1.0.0
~~TensorFlow (r0.12)~~
- Please note that you will get errors if running with TensorFlow r1.0 because the definition of input arguments of some functions have changed, e.g., tf.concat and tf.nn.sigmoid_cross_entropy_with_logits.
The code is updated to run with Tensorflow 1.7.0, and an initial model is provided to better initialize the network. The old version is backed up to the folder old_version.

Datasets

FGNET
UTKFace (Access from the Github link or the Wiki link)

Prepare the training dataset

You may use any dataset with labels of age and gender. In this demo, we use the UTKFace dataset. It is better to use aligned and cropped faces. Please save and unzip UTKFace.tar.gz to the folder data.

Facial Age Progression Software

Training

The training process has been tested on NVIDIA TITAN X (12GB). The training time for 50 epochs on UTKFace (23,708 images in the size of 128x128x3) is about two and a half hours.

During training, a new folder named save will be created, including four sub-folders: summary, samples, test, and checkpoint.

samples saves the reconstructed faces at each epoch.
test saves the testing results at each epoch (generated faces at different ages based on input faces).
checkpoint saves the model.
summary saves the batch-wise losses and intermediate outputs. To visualize the summary,

After training, you can check the folders samples and test to visualize the reconstruction and testing performance, respectively. The following shows the reconstruction (left) and testing (right) results. The first row in the reconstruction results (left) are testing samples that yield the testing results (right) in the age ascending order from top to bottom.

The reconstruction loss vs. epoch is shown below, which was passed through a low-pass filter for visualization purpose. The original record is saved in folder summary.

Custom Training

Testing

Facial Age Regression Software

Note: savedir specifies the model name saved in the training. By default, the trained model is saved in the folder save (i.e., the model name).Then, it is supposed to print out the following message.

Specifically, the testing faces will be processed twice, being considered as male and female, respectively. Therefore, the saved files are named test_as_male.png and test_as_female.png, respectively. To achieve better results, it is necessary to train on a large and diverse dataset.

A demo of training process

The first row shows the input faces of different ages, and the other rows show the improvement of the output faces at every other epoch. From top to bottom, the output faces are in the age ascending order.

Files

FaceAging.py is a class that builds and initializes the model, and implements training and testing related stuff
ops.py consists of functions called FaceAging.py to implement options of convolution, deconvolution, fully connection, leaky ReLU, load and save images.
main.py demonstrates FaceAging.py.

Citation

Zhifei Zhang, Yang Song, and Hairong Qi. “Age Progression/Regression by Conditional Adversarial Autoencoder.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

From tech neck to blue-light eyebags what being glued to technology does to your skin

Total internet hits have surged 70 per cent since lockdown began, says research firm Informa Tech. And it’s not just grey roots that could be ageing you, but screen time too.

So just what can we do to avoid tech neck and blue-light eyebags?

Yasmin Harisha asks Dr Jonquille Chantrey, an aesthetic skin surgeon, and dermatologist RGN Emma Coleman about screen-time skin and how we can combat it.

Plus, with the help of experts at Change My Face, we show you here how screen-time damage might alter Yasmin’s face. The artists use their extensive anatomical knowledge and special software to digitally add filters and layers.

Squint hint

Staring at the screen can cause us to squint resulting in fine lines, or crow’s feet, which can become permanent.

Dr Chantrey recommends an eye test, to see if there is a root cause to your squinting.

Emma says: “The skin around the eyes is very thin. Avoid dark circles by putting your phone away before bed.

“Use a good eye product like Clinique Moisture Surge Eye (£27.50, clinique.co.uk) to keep skin conditioned. I recommend eye gels for saggy skin and creams for hollow under-eyes.”

Selfie shielding

Similar to the sun’s UV rays, blue light is emitted from our technology devices.

Dr Chantrey says: “Blue light can cause spots of age around the face as it can affect melanogenesis, the production of pigment in skin cells. The area mostly exposed to blue light can develop fine lines.”

Emma says: “Signs may include sagging and formation of creases under the eyes.

“Wearing sunscreen daily is the best way to prevent wrinkles and helps to protect against UV rays and blue light.”

Spot the phone addict

If you have tried every remedy under the sun and are still getting spots, it could be caused by your mobile phone.

Dr Chantrey says: “Often I see areas of acne exactly where my patients hold their phone to their faces as phones accumulate significant amounts of bacteria.”

Emma says: “I recommend using products with AHA or BHA acids. The Ordinary lactic acid 5% + HA 2% (£5.50, theordinary.deciem.com) helps keep pores clear and helps spots come to a head.”

Ear-ring aid

Listening through headphones at high volumes can damage the ears and cause inflammation around them or on the neck.

Dr Chantrey says: “Some patients may develop contact dermatitis from some components such as nickel, which can cause inflammation around the ear and neck area.

“If you have found inflammation, or redness, contact your GP to get a full diagnosis. We must clean our ear pieces as invisible bacteria can build up, which causes skin reactions.”

Resting light face

The light from phone screens at night can delay your body’s internal clock, making it more difficult to fall asleep.

Dr Chantrey says: “The bright artificial light can supress sleep-inducing hormone melatonin. I would recommend changing the settings to a night cycle.”

Emma says: “Lack of sleep can cause lacklustre skin. I recommend the Inkey list retinol serum 30ml (£9.99, lookfantastic.com) which will gently exfoliate away pigmentation as you sleep.”

Tech neck

Always looking down at your phone to text or scroll through social media? You could be causing extra wrinkles on your neck.

Dr Chantrey says: “Certain types of neck movement can drag the jawline down quicker and create vertical bands on the neck.”

Emma adds: