This Skin Face Analyzer API allows you to assess skin condition from facial images with high accuracy. When you upload a photo, the system applies computer vision and artificial intelligence algorithms to identify imperfections, wrinkles, blemishes, acne, enlarged pores, hydration levels, and other metrics relevant to skin care.
The API generates structured results in a clear and easily integrable format, including heat maps, severity indices, and percentages of affected areas of the face. This allows for a detailed understanding of skin condition and periodic follow-ups to evaluate the progress or effectiveness of treatments.
It offers objective, automated analysis, eliminates subjective variability, and provides quantifiable data on skin health.
In addition, the API includes facial segmentation options to identify specific regions (forehead, cheeks, nose, chin), offering a localized diagnosis. It also supports parameter customization to adapt to different skin types and lighting environments.
In short, this API turns a simple photograph into a detailed dermatological analysis, helping to provide informed recommendations, improve user relationships, and generate added value through accurate skin data.
Get a complete analysis of the skin you indicate.
Skin Analysis - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] Json |
{"log_id":"1776444169,7f33f409-61d2-4af7-a38b-a5a81a30a1f7","request_id":"1776444169,0f55ec05-37f1-43c6-a510-ef24dd51df0c","timestamp":"2026-04-17T16:42:49.350404","analysis_type":"comprehensive","focus_areas":["acne","wrinkles","pores"],"image_url":"https://a.files.bbci.co.uk/worldservice/live/assets/images/2016/04/21/160421151857_acne_624x351_thinkstock_nocredit.jpg","image_info":{"original_size":{"width":512,"height":288},"processed_size":{"width":512,"height":288},"bbox_format":"x1,y1,x2,y2","coordinate_system":"pixels"},"quality":{"blur_score":0.824,"exposure_score":0.16,"contrast_score":0.294,"overall_quality":"poor","quality_score":0.333,"warnings":["High blur detected - texture-dependent analysis may be unreliable","Consider retaking photo with better focus","Underexposed image - may affect lesion detection"],"scales":{"blur_score":"0=sharp, 1=blurry","exposure_score":"0=dark, 1=overexposed","contrast_score":"0=low, 1=high","quality_score":"0=poor, 1=excellent"}},"face_regions":{"left_cheek":[115,86,201,173],"right_cheek":[288,86,375,173],"chin":[180,173,310,260],"forehead":[180,0,310,86]},"lesions":{"count":0,"severity":"none","severity_percentage":0.0,"confidence":0.95,"detection_status":"not_present"},"pores":{"left_cheek":{"count":1,"density":1.34,"density_units":"pores/10k_pixels","severity":"low","confidence":0.600133654103181,"filtering_applied":"morphological + circularity"},"right_cheek":{"count":7,"density":9.25,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6009248249438499,"filtering_applied":"morphological + circularity"},"chin":{"count":2,"density":1.77,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6001768346595933,"filtering_applied":"morphological + circularity"},"forehead":{"count":1,"density":0.89,"density_units":"pores/10k_pixels","severity":"low","confidence":0.6000894454382826,"filtering_applied":"morphological + circularity"}},"wrinkles":{"left_cheek":{"wrinkle_score":0.546,"severity":"moderate","confidence":0.8638320685224598},"right_cheek":{"wrinkle_score":0.37,"severity":"moderate","confidence":0.8111346385265074},"chin":{"wrinkle_score":0.444,"severity":"moderate","confidence":0.8332612127886169},"forehead":{"wrinkle_score":0.585,"severity":"moderate","confidence":0.8756066997274834}},"pigmentation":{"left_cheek":{"spot_count":1,"density":1.34,"density_units":"spots/10k_pixels","severity":"none","confidence":0.600133654103181,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"right_cheek":{"spot_count":1,"density":1.32,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6001321178491213,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"chin":{"spot_count":0,"density":0.0,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"},"forehead":{"spot_count":1,"density":0.89,"density_units":"spots/10k_pixels","severity":"none","confidence":0.6000894454382826,"filtering_applied":"morphological + circularity","detection_type":"defined_spots_only"}},"skin_type":{"label":"mixed","confidence":0.8,"texture_score":17442.5879},"severity":{"overall":"mild","confidence":0.703,"component_scores":{"inflammatory_acne":0,"pores":0.2,"wrinkles":0.7,"pigmentation":0.0},"total_weighted_score":0.9,"weighting_system":"mature_skin_optimized","explanation":"Wrinkles and pigmentation weighted higher for mature skin analysis","criteria":{"inflammatory_acne":">5 lesions or >2% area","pores":">300 pores/10k_pixels in any region","wrinkles":">0.6 wrinkle_score in any region","pigmentation":">500 spots/10k_pixels in any region","thresholds":{"mild":"0-2 lesions, <100 pores/10k_pixels, <0.3 wrinkle_score","moderate":"3-5 lesions, 100-300 pores/10k_pixels, 0.3-0.6 wrinkle_score","severe":">5 lesions, >300 pores/10k_pixels, >0.6 wrinkle_score"}}}}
curl --location --request POST 'https://zylalabs.com/api/9328/skin+face+analyzer+api/16871/skin+analysis' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"analysis_type": "comprehensive",
"image_url": "https://a.files.bbci.co.uk/worldservice/live/assets/images/2016/04/21/160421151857_acne_624x351_thinkstock_nocredit.jpg",
"focus_areas": ["acne", "wrinkles", "pores"]
}'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Skin Analysis endpoint returns detailed metrics on skin conditions, including imperfections, wrinkles, blemishes, acne, enlarged pores, and hydration levels. It provides structured results such as heat maps, severity indices, and percentages of affected areas.
Key fields in the response data include "imperfections," "wrinkles," "blemishes," "hydration level," and "affected area percentages." Each field provides specific insights into the skin's condition, allowing for targeted analysis.
The response data is organized in a structured JSON format, with sections for overall skin analysis, localized regions (forehead, cheeks, etc.), and visual representations like heat maps. This structure facilitates easy integration into applications.
Users can customize their requests by specifying parameters such as image quality, skin type, and lighting conditions. This allows for tailored analysis based on individual user needs and environmental factors.
Data accuracy is maintained through advanced computer vision and AI algorithms that analyze facial images. Continuous updates and training on diverse datasets ensure reliable and precise skin condition assessments.
Users can customize their requests by specifying parameters such as skin type and lighting conditions. This allows the API to tailor the analysis to different skin characteristics, enhancing the accuracy of the results.
Typical use cases include personalized skincare recommendations, tracking skin health over time, and assisting dermatologists in diagnosing conditions. The API can also enhance user engagement in cosmetic applications.
Users can utilize the returned data by integrating it into skincare applications, generating reports for dermatologists, or creating personalized skincare plans based on the severity indices and affected area percentages.
Users can expect data patterns that highlight common skin issues, such as higher percentages of blemishes in oily skin types or increased wrinkles with age. These patterns help in identifying trends and tailoring skincare solutions.
Quality checks include algorithm validation against clinical data, regular performance evaluations, and user feedback mechanisms to ensure the accuracy and reliability of the skin analysis results.
The Skin Analysis endpoint can detect various skin conditions, including imperfections, wrinkles, blemishes, acne, enlarged pores, and hydration levels. This comprehensive analysis helps users understand their skin health and identify areas needing attention.
The response includes localized analysis for specific facial regions such as the forehead, cheeks, nose, and chin. Each region is assessed individually, providing targeted insights into skin conditions and allowing for more precise recommendations.
The analysis results include visual representations like heat maps that highlight affected areas, along with severity indices that quantify the extent of skin issues. These visuals enhance understanding and facilitate better decision-making for skincare.
Users can customize their analysis by specifying parameters such as skin type (oily, dry, combination) and lighting conditions (natural, artificial). This ensures the analysis is tailored to individual skin characteristics and environmental factors.
Severity indices indicate the intensity of skin conditions, with higher values representing more significant issues. Users can utilize these indices to prioritize treatment areas and track improvements over time.
If the analysis returns incomplete data, users should check the image quality and ensure it meets the API's requirements. Adjusting parameters like lighting or skin type may also improve results in subsequent requests.
The API supports common image formats such as JPEG and PNG for uploads. Ensuring the image is in one of these formats is essential for successful analysis.
The API is designed to accommodate a wide range of skin tones by utilizing diverse training datasets. This ensures accurate analysis across different skin types, enhancing the reliability of the results for all users.
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