How to Know What Face Shape You Have Using AI

What is a Face Shape Detector? 

An AI-powered tool that detects and analyzes facial features such as the shape of face, eyebrows, lips, eyes and others is considered a face shape detection system. It detects the face in a selfie portrait or photograph that is fed into the system and performs the analysis. The tool measures the distance between key points such as forehead, jawline, chin and cheekbones to determine the face shape. Individual users, clients and retail customers can understand their face shape to choose from various types of spectacles, makeup, jewelry, hairstyles and more. As one of the important FMCG beauty technology solutions, it assists brands in offering personalized solutions, skin care treatments, virtual try on services and related recommendations. In general, it is also used for anthropometric studies, cosmetic surgery and art. 

Face shape detector domain is considered a subfield of facial alignment technology. This market is surging at a CAGR of 14.8% from the initial value of US $4.35 billion as of 2019 to approximate value of US $19.92 billion by 2032. Also, the facial recognition market was valued at US $4.03 billion as of 2024 and is expected to reach a market value of US $35.91 billion by 2033 increasing at a CAGR of 17.1% during this forecast period. An additional space related to three-dimensional facial scanners that capture the 3D images of faces using structured lights and laser scanning can also fall under these technologies apart from AI skin analysis. These scanners create digital models by analyzing face shape, features, textures and contours. This market was valued at US $4.2 billion in 2024 and is expected to grow up to US $15.4 billion by 2033, rising at a CAGR of 15.6% during this period. 

Growing market size of face detection, alignment and face type detector from 2022 to 2032   

Types of Face Shape Analyzers 

Face shapes can be effectively determined by considering facial attributes, skin tone and facial ratio. The following are the types of face shapes that are commonly recognized by these analyzers:  

• Round: This type of face features soft, curved lines across the face length with cheekbones that are approximately of the same width, and a rounded chin. 

• Oval: This oval face type has more length than width and has gentle curves at the sides representing equal proportions. The forehead is slightly wider and tapers to gently round chin in this case. 

• Heart: This type of face appears similar to an inverted triangle. It features a broader, wider forehead and high cheekbones resembling the oval shape with a narrow, pointed chin. 

• Oblong: The oblong face type features a jawline, straight sides, cheekbones and a forehead of similar width, and the face appears long as compared to wide with a narrow, pointed chin. 

• Diamond: This type of face features high cheekbones as the widest part alongside a narrow forehead and jawline, while the chin appears pointed and tapered for an angular look. 

• Square: In this case typically, the broad forehead and the strong, angular jawline are roughly of the same width across the face. 

Facial ratio is another important aspect of the functioning of face shape analyzer, symmetry and aesthetics effectively. It refers to the proportion between different parts of the face, thus given below are the different types of facial ratios: 

• Facial Height to Width Ratio: It is the total height from the hairline to chin versus the width across the cheekbones.  It is also known as face horizontal ratio or the face aspect ratio that is the width of the face and its height as is ideally 1:1.46. 

• Upper to Lower Facial Height Ratio: In this case, the upper facial height refers to the distance between hairline to base of the nose. The lower facial height is the distance between the base of the nose to chin and the ratio between the former and the latter is roughly equal or 1:1. 

• Eye width to Interocular Distance Ratio: It is basically the space between the eyes where the eye width and the interocular distance is typically equal. It is also called face vertical ratio where the ratio amongst the width of one eye, space between outer eye corners, distance between eyes and the edge of the face is 1:1:1:1:1. 

• Nose Width to Intercanthal Distance Ratio: It refers to the equal distance between inner corners of the eyes and the nose width. 

• Mouth Width to Nose Width Ratio: Usually in this case the mouth width is 1.5 times the width of the nose. 

• Chin Height to Lip Height Ratio: This chin height is typically two times the height of the lips. 

• Eyebrow to Eye Distance to Eye Height: This ratio is supposed to be balanced when considering expressions and aesthetics. The eye aspect ratio considers the relationship between the height of the eye and its width, which is ideally 1:3. 

• Golden Ratio: It is considered as the perfect, ideal aesthetic proportion that is found commonly in many facial features, and it is equal to 1:1.618 (φ). 

Different types of facial ratios detected by face shape analyzer 

Considering the above-mentioned context, these analyzers can be of the following types: 

• Golden Ratio Face Tool: It compares the face captured and features analyzed to the Golden Ratio for scoring attractiveness. It is used by cosmetic surgeons and beauty professionals as well. 

• Face Symmetry Test: It is basically a test of left and right symmetry of the face that highlights the proportional imbalances for attractiveness scoring based on proportions and features. 

• Simple Shape Classification: It segregates the face shape into diamond, hearty, round, oval, square etc. It is useful for detecting face shapes and hairstyles as per the detected shape. 

• AI-based Shape Detection: It is usually found in smart mirrors, smart beauty applications, AR beauty tools, web face shape analysis applications, It detects and recommends personalized hairstyling and virtual makeup for individual customers. 

Working of Face Shape Detector 

Let us go through the pipeline overview of the functioning of these detection engines: 

1. Image Input: A photograph or selfie taken under good lighting showcasing frontal face with minimal occlusion is uploaded to the system via the camera. 

2. Face Detection & Alignment: The face is detected in the image using tools such as Haar Cascades (OpenCV), Multi-task Cascaded Convolutional Neural Network (MTCNN), CNN face detector or Dlib HOG, Google Media Pipe Face Detection, and further aligned for processing. 

3. Facial Landmark Detection: The system detects the key points such as chin, jawline, cheekbones, nose, mouth, eyes etc., using models such as Dlib’s 68-point facial landmark predictor, Facial Alignment Network (FAN) and Media Pipe Face Mesh with 468 landmarks. These can especially be useful to determine hairstyles for your face shape. 

4. Feature Extraction: The distances and angles such as jaw width, cheekbone width, forehead width, face length etc. are computed alongside ratios such as width-to-length, jaw-to-cheekbone, forehead taper and others. For example, a system powered by Media Pipe and Random Forest can detect facial landmarks in about 100-150 ms, features can be extracted in 100-200 ms, calculation of features can be done in about 20 ms whereas the shape can be classified in about 10 ms, making a total of approximately 300 to 400 ms. 

5. Shape Analysis: The facial features are put into a classifier engine which can be a machine or deep learning model (MobileNet, ResNet, high accuracy, heavy in nature) such as Support Vector Machine (SVM), Random Forest (Scikit-learn), K-Nearest Neighbors (KNN, Logistic Regression for faster classification and used with extracted ratios), Shallow Neural Network, end-to-end CNNs (MTCNN, Media Pipe, accurate, fast and lightweight) for mobile applications etc. 

6. Hardware & Software Requirements: GPU such as NVIDIA GTX 1050+, Android Neural Networks API, Apple CoreML for mobile inference etc. can speed up CNN models in real-time. Quad-core CPU with 16 GB RAM allows moderate speed in real-time of their working with respect to landmark extraction and classification of landmarks. The average inference time is 0.3 to 0.2 as per image as per model size and resolution of the image. We have already discussed regarding the software stack, libraries and frameworks such as Python-based tools like Dlib, face recognition with OpenCV, Scikit-learn, TensorFlow, PyTorch, mobile SDKs like Media Pipe (Android/iOS), CoreML, MLKit and web-based tools such as JavaScript, TensorFlow.js, Face API etc. 

Use Cases of Face Type Detector 

Let us dive into the various practical use cases of these detectors in multiple industries: 

Beauty Care 

These detectors are used in beauty applications for recommendations of hairstyles, spectacle frames, contouring procedure, eyebrow shapes as per the analyzed customer’s face. Personalized virtual makeup, accessory and jewelry try-ons can enhance user experience and ultimate sales. They are also used for non-invasive facial fat and volume tracking in fitness applications to estimate the progress of fat loss or gain over a specific period of time. The choice of hairstyle enhances the overall appearance through balanced proportions where the best features are emphasized, and others are softened. The following pointers can guide a user to relate face shapes and hairstyles as per conducted analysis: 

• Oval: Representing balanced proportions, wider cheekbones and narrowing jaw and forehead, long wave blunt bobs, layered cuts and side swept bangs are preferred, while heavy bangs must be avoided as hairstyles for your face shape. 

• Round: Showcases equal width and height with a soft jawline and full cheeks, the face needs to appear elongated with added angles as per common public opinion. Thus, long layered styles, side parts, high ponytails, pixie cuts with more volume on top are preferred, while straight, chin-length bobs, blunt bangs must be avoided. 

• Square: Featuring broad forehead, angular jawline, equal width of cheekbones, jaw and forehead, soft curls, long layers, side-swept bangs, wavy bobs and shoulder-length cuts can be preferred, while straight blunt bobs, center parts and angular cuts must be avoided. 

• Heart: Representing broad forehead, cheekbones, narrow jawline and pointed chin, side-swept bangs, chin-length bobs, long layers, soft curls and deep side parts are preferred, while short bangs, slicked-back styles and cuts must be avoided. 

• Diamond: Showcasing narrow forehead, chin with wide cheekbones, chin-length bobs, long side-swept bangs, deep side parts and soft curls are best suited, while on the sides and extremely short cuts must be avoided 

• Oblong: Long rectangular faces that are wide, and have straight sides, long chin are best suited with soft curls, curtain bangs, layered shoulder cuts, blunt cuts and voluminous waves, while long straight hair without layers and short styles must be avoided. 

• Triangle: Pear-shaped faces with narrow forehead and wide jawline look great with side-sept bangs, volume at the crown layered pixies and bobs with volume at the top, while heavy, blunt cuts near the jawline or styles need to be avoided. 

Determination of various types of hairstyles for your face shape 

Fashion & Retail 

Face analyzers can recommend spectacles, goggles, glasses, sunglasses etc. that suit the customers’ facial structure, for example, round glasses for square faces. Face shape data which is collected and analyzed by the system can be used to create custom-fit products such as hats, masks, earrings etc. 

Dermatology 

Analysis provided by these detectors can be used by surgeons for pre-surgery visualization and simulation of cosmetic changes including jawline reshaping, rhinoplasty as well as for prediction of post-surgical outcomes. It helps professionals in the conduction of aesthetic planning and determination of ideal proportions, suggestion of minimally invasive treatments such as Botox and fillers. 

Entertainment 

Apart from virtual try-ons in the retail brick and mortar or web stores, e-commerce platforms can leverage these analyzers to improve product recommendation via virtual try on clothes, cosmetics and jewelry. Moreover, virtual or realistic avatars in the gaming industry’s metaverse environments can match the real facial features of the gamer. 

Leverage KritiKal’s Advanced Face Type Detector 

In this blog, we discussed various types of face detectors and shape analyzers, their use cases, functioning, requirements, as well as different types of facial attributes and ratios. KritiKal offers detectors for face shapes and hairstyles, that can analyze facial features including forehead. jawline, cheekbones, chin, eyes, nose, mouth etc. accurately. Advanced AI models detect faces in selfies and calculate required ratios that help businesses to offer personalized hairstyles, makeup, eyewear, and accessories. Over the years, it has supported multiple industries and technologies such as beauty, ecommerce app development, healthcare and gaming. Not only do these detectors enhance customer experience through virtual try-ons and aesthetic planning but also enable various healthcare applications. Please get in touch with us at sales@kritikalsolutions.com to know more about our products and realize your beauty tech requirements. 

Sai Teja

Sai Teja currently works as a Software Engineer at KritiKal Solutions. He is proficiently skilled in NLP, image processing, LLM, OCR, table parsing, detection and more techniques. With an advanced understanding of statistical, algebraic, analytical techniques, he showcases apt motivation and diligence in assisting KritiKal with delivering various projects to some major clients.