Predicting Ideal Hairstyles Based on Face Shape | Beauty Machine Learning

Machine learning systems can predict the most flattering hairstyles based on an individual's facial shape, using facial recognition classifiers to identify features like eye size and jaw shape. By categorizing faces into oblong, round, oval, square, and heart shapes, the technology recommends hairstyles that complement each unique face structure, considering aspects such as hair length, style, fringe, and layering. The goal is to enhance facial aesthetics by adhering to geometric rules and scientific evidence, with an emphasis on inclusivity and addressing ethnic bias in AI beauty tools.

Takeaways

• 🧑‍🔬 Facial identification systems use machine learning to recommend hairstyles based on facial morphology.
• 👁️ Ocular morphology, jaw shape, and overall facial shape are key factors in determining the most flattering hairstyles.
• 📜 A 2018 paper by Pasupa et al. describes a support vector machine for recommending hairstyles based on scientific aesthetics.
• 📏 Face shapes are categorized into five main types: oblong, round, oval, square, and heart, each with distinct characteristics.
• 💡 Oblong faces, like those of Sarah Jessica Parker and Alexa Chung, have equal width along the forehead, cheeks, and jawline.
• 🍎 Round faces, exemplified by Jennifer Lawrence, have the widest part at the cheekbones and a significantly curved chin.
• 🥚 Oval faces, like Blake Lively's, are longer than they are wide with the cheekbones as the widest part.
• 🔲 Square faces have equal height and width, with very straight sides and sharp features, as demonstrated by Cameron Diaz.
• 🧡 Heart-shaped faces are widest at the forehead and have a pointed chin, often with a widow's peak.
• 📊 Machine learning systems use geometrical rules and ratios to identify facial shapes and recommend suitable hairstyles.
• 🌐 The technology addresses ethnic bias by training on diverse datasets like the Fairface dataset, which includes seven racial groups.
• 💇‍♀️ Recommendations for each face shape focus on hair length, style, fringe, and layering to complement facial features.

Q & A

How does facial identification system recommend hairstyles based on face shape?

Facial identification systems use machine learning to analyze facial morphology, including ocular morphology, jaw shape, and cheekbones. They can recommend hairstyles by evaluating the overall facial shape using a facial recognition classifier.

What is the basis of the technology that divides face shape into categories?

The technology is based on dividing face shapes into five main categories: oblong, round, oval, square, and heart, each defined by specific characteristics of the forehead, cheekbones, jawline, and chin.

What are the characteristics of an oblong or rectangular face shape?

An oblong or rectangular face shape is characterized by a long thin face with equal width for the forehead, cheeks, and jawline, and a chin with a very slight curve. Oblong faces are also known for having sides with a very similar length and a slight curve in the chin.

How is a round face shape characterized?

A round face shape is characterized by a very circular head, with the cheekbones being the widest part of the face. The chin is significantly curved, and the sides of the face curve outwards, instead of being straight.

What are the features of an oval face shape?

An oval face shape is characterized by a tall forehead, with cheekbones as the widest part of the face, and an overall face that is longer than it is wide. The difference in length between the forehead and cheekbones is smaller compared to a round face.

What are the attributes that the support vector machine recommends hairstyles on?

The support vector machine recommends hairstyles based on four attributes: length (pixie, short, mid, long), style (straight, wavy, or a mix), fringe (no fringe, straight fringe, side swept fringe), and whether the hair should be layered or not.

How does the Active Appearance Model technology contribute to face shape recognition?

The Active Appearance Model technology contributes by looking at vital points on the face and skin pigmentation to separate the hair and forehead, allowing for the calculation of geometric features that represent face shape.

What are the limitations of the current face shape classifier in terms of ethnic bias?

The current face shape classifier may have limitations regarding ethnic bias due to a lack of diverse training data. The paper mentioned uses only East Asian faces for training, but the developers aim to use more diverse datasets like the Fairface dataset, which includes equal weightings of 7 racial groups.

What is the role of the Fairface dataset in developing AI tools?

The Fairface dataset is used to train AI tools with equal weightings of 7 racial groups, aiming to reduce ethnic bias and increase inclusivity in the machine learning field.

How does the technology map the recommended hairstyle onto the face?

The technology maps the recommended hairstyle onto the face using specific reference points related to the forehead, chin, and cheekbones to ensure the hairstyle complements the specific features of the face.

What are some additional face shapes that were mentioned in the script?

In addition to the five main face shapes, the script also mentions the diamond shape, characterized by a narrow hairline, high cheekbones, and a pointed chin, and the triangular face shape, which has a broad face shape tapering down to a much narrower jawline and chin.

What is the significance of the geometrical rules defined through aesthetics guidelines in hairstyle recommendations?

The geometrical rules defined through aesthetics guidelines provide objective reasoning for which hairstyles work best for different face shapes. This objectivity allows machine learning systems to identify facial shapes quickly and recommend the most suitable hairstyles based on the geometrical features of the face.

Outlines

00:00

🤖 Machine Learning and Facial Morphology for Hairstyle Recommendations

This paragraph discusses how facial identification systems can predict the most flattering hairstyles based on an individual's facial shape. It explains that machine learning systems can objectively determine which features will suit a person's face by evaluating various aspects of their facial morphology, such as eye size and shape, jaw shape, and cheekbones. The paragraph also introduces a support vector machine developed by Pasupa et al. in 2018, which recommends hairstyles based on scientific aesthetics evidence. It outlines the categorization of face shapes into five main types: oblong, round, oval, square, and heart, providing examples of celebrities for each shape. The technology's foundation lies in dividing face shapes into these categories to recommend certain features that would best suit an individual's face.

05:01

🧑‍🔬 Facial Shape Classification and Hairstyle Suitability

The second paragraph delves into the nuances of different face shapes, acknowledging the limitations of categorizing facial diversity into five main types. It mentions additional shapes like the diamond and triangular face shapes. The paragraph provides scientific evidence that suggests each unique face shape has a set of hairstyles that best complement it. It describes the development of a facial shape recognition classifier trained using 1000 faces sourced from a Google image search. The process of training involved comparing manual labeling by volunteers to the facial shape recognition system. The technology uses an 'Active Appearance Model' to analyze vital points on the face and geometric features to determine the most suitable hairstyles. The paragraph also addresses the issue of ethnic bias in AI technology and mentions the use of the Fairface dataset for training to ensure inclusivity. It outlines the attributes the support vector machine uses to recommend hairstyles: length, style, fringe, and layering. Recommendations are provided for round face shapes, emphasizing the creation of extra height and avoiding tucking hair behind the ears.

10:04

💇‍♀️ Tailoring Hairstyles to Complement Specific Face Shapes

The final paragraph focuses on how hairstyles can be tailored to complement specific face shapes, providing detailed advice for each of the five main face shapes. For square faces, it recommends hairstyles that soften the sharp features and adds height, avoiding strong, straight fringes. Oval faces, considered versatile, can pull off a wide range of hairstyles without needing to add extra height. Oblong or rectangular faces are suggested to have curly or short hair to reduce the perceived height and create a fuller look. Heart-shaped faces are advised to maintain width with a side swept fringe and opt for long, layered hair, which can be either curly or straight. The paragraph emphasizes that facial shapes can be objectively defined by examining the skull's morphology, particularly the chin, cheekbones, and forehead, and their relationships. It concludes by stating that specific hairstyles focusing on hair length, style, fringe, and layers can enhance the unique features of an individual's face.

Keywords

Machine Learning

Machine Learning is a type of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. In the context of the video, machine learning systems are used to analyze facial features and recommend hairstyles that best suit an individual's face shape. It's a core concept as it forms the basis for the technology being discussed.

Facial Morphology

Facial morphology refers to the shape and structure of a person's face. It includes the size and shape of various facial features such as eyes, jaw, and cheekbones. In the video, facial morphology is used to predict which hairstyles will be most flattering for a person's unique face shape, which is central to the theme of the video.

Facial Recognition Classifier

A facial recognition classifier is a machine learning model that can identify and categorize faces based on their unique features. In the video, it is used to define an individual's facial shape, which is then used to recommend hairstyles. It's a key technology in the process of predicting ideal hairstyles.

Support Vector Machine (SVM)

A Support Vector Machine is a supervised learning model used for classification and regression analysis. In the context of the video, an SVM is constructed to recommend a chosen hairstyle for a user based on scientific aesthetics evidence. It's an integral part of the system that helps predict hairstyles.

Face Shape Categories

The video outlines five main categories of face shapes: oblong, round, oval, square, and heart. Each category has distinct characteristics that influence the type of hairstyles that would be most suitable. Understanding these categories is crucial for the machine learning system to make accurate hairstyle recommendations.

Ocular Morphology

Ocular morphology pertains to the size, shape, and structure of the eyes, which includes the skull composition around the eyes, eye color, and eyelid shape. It is one of the many factors considered by the machine learning system when predicting the most flattering hairstyles, as it contributes to the overall balance and aesthetics of the face.

Jaw Shape

Jaw shape is a significant aspect of facial morphology that influences the choice of hairstyles. It refers to the structure and outline of the jawline and how it complements other facial features like teeth and cheekbones. In the video, jaw shape is evaluated to determine which hairstyles would best suit an individual's face.

Hairstyle Recommendations

The video discusses how machine learning systems can recommend hairstyles based on an individual's facial shape and features. The recommendations consider the length, style, fringe, and layering of the hair to enhance the specific features of the face. These recommendations are a direct application of the machine learning system's analysis.

Ethnic Bias in AI

Ethnic bias in AI refers to the unfairness or inaccuracies that can arise in AI systems due to a lack of diverse training data. The video acknowledges this issue, noting that the current face shape classifier is trained using a dataset with equal weightings of different racial groups to minimize bias. Addressing ethnic bias is important for the fairness and inclusivity of AI tools.

Active Appearance Model (AAM)

The Active Appearance Model is a technique used for landmark localization on faces. It examines vital points on the face and skin pigmentation to separate the hair and forehead, allowing for the calculation of geometric features that represent face shape. AAM is a key component in the technology that enables the accurate recommendation of hairstyles.

Hairstyle Mapping

Hairstyle mapping is the process of applying a recommended hairstyle onto a person's face using specific reference points related to facial features like the forehead, chin, and cheekbones. This technique is used in the video to demonstrate how a chosen hairstyle will look on an individual, which is a practical application of the machine learning system's predictions.

Highlights

Machine learning can predict the most flattering hairstyles based on an individual's facial shape.

Facial identification systems use facial morphology to recommend hairstyles that best suit a person's face.

Features like ocular morphology, jaw shape, and cheekbones are evaluated to determine the ideal hairstyle.

A facial recognition classifier defines overall facial shape, which is crucial for hairstyle recommendations.

A 2018 paper by Pasupa et al. outlines a support vector machine for recommending hairstyles based on scientific aesthetics.

The technology allows users to 'wear' a hairstyle virtually to see how they look with it.

Face shapes are categorized into five main types: oblong, round, oval, square, and heart.

Oblong faces, like those of Sarah Jessica Parker and Alexa Chung, are characterized by equal width along the forehead, cheeks, and jawline.

Round faces, exemplified by Jennifer Lawrence, have the widest part at the cheekbones with a significantly curved chin.

Oval faces, like Blake Lively's, are longer than they are wide with the cheekbones as the widest part.

Square faces, such as Cameron Diaz's, have equal height and width with very straight sides and a sharp jawline.

Heart-shaped faces, with the widest part being the forehead and a pointed chin, are also considered.

Beyond the five main categories, other shapes like diamond and triangular faces are acknowledged.

Scientific evidence suggests specific hairstyles complement each unique face shape based on geometrical rules.

The facial shape recognition classifier is trained using 1000 faces sourced from a Google image search.

The Active Appearance Model technology is used to calculate geometric features representing face shape.

Ethnic bias in AI is a concern, and the Fairface dataset with equal weightings of 7 racial groups is used to mitigate this.

The Support Vector Machine recommends hairstyles based on length, style, fringe, and layering attributes.

For round faces, medium-length hairstyles with a side-swept fringe are suggested to add height and avoid roundness.

Square faces benefit from hairstyles that soften their sharp features and add perceived height.

Oval faces are versatile and can pull off a wide variety of hairstyles without needing to alter face shape proportions.

For oblong faces, curly or short hairstyles are recommended to reduce face height and create a fuller look.

Heart-shaped faces are advised to maintain width with a side-swept fringe and opt for long, layered hairstyles.