**5. Conclusion**

We have presented in this chapter an interesting application of deep learning in aesthetic surgery recommendation along with its encouraging results. By using our system, the presented deep learning engine will provide a reference decision of taking either rejuvenation treatment or eye double-fold surgery or not to both the surgeon and the patient, just based on the eye photo of the patient. To this end, we trained a deep autoencoder by our in-house dataset, composing of pairs of images captured before and after the surgery. The trained encoder part learned in an unsupervised manner, a rich set of features, characterized both unattractive and beautiful facial features. We concatenate the trained encoder part to a fully connected layer to predict perfection score of an eye photo of a patient, based on which a decision of taking treatment or not will be made.

#### *Advanced Analytics and Artificial Intelligence Applications*

Even though our preliminary results are promising with 88.9 and 93.1% accuracy on rejuvenation treatment and eye double-fold surgery, respectively, it still has much rooms for improvement. Firstly, we should improve the dimension of our in-house dataset by encouraging more patients to participate in our program, so that we are able to build a deeper network. More and more layers are added; richer and richer learned features are obtained to improve the accuracy of our system. Secondly, we are going to expand the capability of our system to deal with more kinds of treatments to diversify and provide the best services to our clients, rather than focusing on the two above treatment and surgery.
