A Hairstyle Recommender System Using Machine Learning
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hAIr: A Hairstyle Recommender System Using Machine Learning Sark Xing Ward de Groot Abstract Technical University of Eindhoven Technical University of Eindhoven Choosing a new hairstyle can be a difficult, impactful [email protected] [email protected] decision. Especially envisioning if a haircut would suit the individual is hard. With the analysis responses from Y izhou Liu Lara Leijtens facial recognition APIs and supervised machine Technical University of Eindhoven Technical University of Eindhoven learning, a relation between facial features and [email protected] [email protected] hairstyle is ought to be found in this project, so that a hairstyle recommender system, called “hAIr”, can be created. The system recommends hairstyles that suit the individual’s characteristics. This is based on a neural network learning algorithm, which is trained with features, extracted from 1060 images of people, relating to 53 different hairstyles. The trained network reaches an accuracy of 28.10% when validated with images that were not used for training. This can be improved by trying different combinations of input variables, or using a different conversion for the values that were gained from the APIs. It is also possible that the APIs are not completely accurate. A third possibility for improvement would be to use a different learning algorithm, such as k-Nearest Neighbors or naive Bayes. Introduction about themselves, once they are presented with an Context option. To decide if they like the style or not can be Going to the hairdresser can come with difficult decision assessed by looking at another person that has that making. A new haircut is a decision that cannot be haircut. However, whether it will suit them personally is changed for at least six weeks. So, for this amount of hard to assess, so this is the challenge that is aimed at. time it is visible to everyone that a wrong decision has been made. Especially for occasions where a good Competition haircut is essential, such as a job interview, the wrong Currently there are several products in development haircut can badly influence a first impression. Not only that are similar to the envisioned product for this the impact of the decision makes it hard to choose, also project. However, not one of them is dominating the the large amount of options causes difficulty. There are market, so there is still room for competitors. so many different hairstyles that it can seem like an impossible decision to make. Face It [18] is an application that detects the shape of person’s face. Hereafter, the user has to provide At a barbershop or hairdresser there are often personal information, such as occupation. Based on magazines provided with examples of haircuts. these combined input features, a recommendation for However, these examples are not personalized. So hairstyle, including facial hair, is given. This differs from when it is boiled down to a selection that fits the the envisioned product, because the user does not need client’s style, it is not guaranteed that the haircuts will to provide any information other than a picture. This look good on the client. This is because a haircut can increases the ease of use. However, Face It has some look different on different people. An experienced additional features that could be added in later barber or hairdresser could give advice on this, but development of the envisioned product, such as a list of they are hard to find and often expensive. nearby hairdressers and recommended hair products. Challenge The app Hair Color of ModiFace [14] focusses on hair The challenge that is ought to be tackled here is to color change. It gives a real time visualization of a make it easier for people to decide on a new hairstyle different hair color mapped on a person’s hair through by minimizing the risk and limiting the options. The live camera input. This could be interesting for system is meant for both men and women of all ages collaboration, since the product of this project does not that want to try something new, but do not know what focus on hair color, but on hair style. haircut would suit their facial features and do not have an experienced hairdresser who can advise them on the Both Dreambit [5] and FaceApp [7] have a functionality matter. Hairstyle does not only correlate with facial to map different hairstyles on images of the user’s face. features, but also with other elements, such as a This is not the area that is currently focused on, but the person’s clothing style or a person’s current hairstyle. technology would be interesting to apply in later However, this is often something that people do know development. Approach if this has led to the correct target (supervised Envisioned solution learning). Depending if this is correct or not, the To complete the challenge, the envisioned solution is an network adjusts the weights of its next guess. The delta app that provides users with a limited amount of rule is applied so that the amount of adjustment is hairstyles which will all suit the user’s face. This will gradually descending. The delta rule is a special case of minimize the risk of choosing a hairstyle that does not the more general backpropagation algorithm that was look good and makes it easier to choose. It is expected used for this project. that it can be determined whether a hairstyle will look good or not on a person, based on facial features and Additionally, other than using a NN, the k-Nearest characteristics such as age and gender. However, this Neighborhood (kNN) algorithm could also be fitting for relation is not easily grasped in a formula or logic rule. this problem. The kNN classification maps every input Therefore, machine learning is used to find matches. in multidimensional space. The position is determined More specifically, supervised machine learning, because by the input features, in this case the shape of the face hairstyles have existing names and do not need to be or the skin color. This position is labeled with the categorized by the machine. target, in this case the hairstyle. When a user provides new input features, a position is again determined Learning algorithm based on their values. Since there is no label provided, As learning algorithm for the system, a Multi Layered the system will check which point or points are closest Perceptron (MLP) is chosen, which is widely used in to it. The amount of points that are taken into account many similar recommender cases to solve realistic is called the k-value, and it is usually a small integer problems [11][17]. An MLP is a type of Neural Network number. So, basically the system finds the person that (NN) that deals with non-linearly separable data. An the user looks like most and advises the same hairstyle artificial neural network is a learning algorithm as this person. consisting of nodes and weighted connections. The nodes are grouped in layers. For an MLP there is one Furthermore, naive Bayes could be used as a classifier input layer that contains the input variables, one or as well. It uses a probabilistic model to check multiple hidden layers and one output layer that independence between features and is based on Bayes’ contains the output targets. theorem. A simplified dependence applied to this case would be: what is the chance of a bobline hairstyle Each node from one layer is connected to all nodes in when a person has an oval face? This can be calculated the next layer. Training is done by using Neuroph [15], by multiplying the chance on a bobline hairstyle by the a lightweight Java neural network framework. During chance on an oval face when someone has a bobline the training, the network looks for the optimal hairstyle, and dividing the answer by the chance that a combination of weights. This works as following. The person has an oval face with any hairstyle. Repeating network takes one person’s input variables and target this for all possible independencies, and combinations, and guesses what the weights could be. Then, it checks e.g. the chance on a bobline when a person has an oval attributes, the researchers of Hairstyle30k manually face ánd a pointy nose, results in a prediction model. coined and annotated 64 labels for different hairstyles. However, amongst the researchers there is no Since all these images are labelled with the target data experience with these two latter algorithms. Therefore, (hairstyle), it is practical to use the same images as it would take a lot of time to find out how the three input. However, the exact input variables need to be algorithms work and design a test to compare the three specified. The aim is to relate facial features to the algorithms. Hence, a pilot test for the NN was hairstyles, so the facial features of the people in the conducted to investigate whether this learning 30k database need to be collected. To achieve this, algorithm would be fitted for the task at hand. there are several options available: 1) looking at the images and noting the features by hand, either done by Data acquisition the researchers or by experts (hairdressers), 2) To train a supervised AI, it must be fed with correct creating a machine learning algorithm that extracts the combinations of input as well as output variables. The features from the images, and 3) using an existing API input variables are facial features and the output that extracts the features.