AI Face Detection Data Tool

@devanshkhandekar

Hi,I’m a seasoned Applied ML Engineer & I’ve built face-analysis pipelines for large personal photo libraries: robust detection, clean embeddings, dedup/identity grouping, and exportable analytics for downstream exploration. Relevant experience * Replaced managed face matching with a custom FaceNet/ArcFace-style embedding pipeline: detect -> align -> embed ->store vectors (Postgres/FAISS) → cosine-similarity matching with tunable thresholds for “same person” clustering. * Worked on improving face detection in real-world photos (varied lighting/pose/occlusion) using RetinaFace/MTCNN-class detectors + post-processing (NMS tuning, min-face filters, blur/quality gating) to keep false positives low * Built production workflows that output : per-image JSON/CSV, cropped/normalized face thumbnails for quick QA & summary stats (faces per photo, top recurring identities, distribution over time/folders). What you’ll get • A Windows CLI script that scans a folder recursively and outputs: * per-image rows: face_count, bounding boxes, quality scores * per-face rows: face_id (cluster), embedding id, thumbnail path * optional tags: coarse age-group/emotion if you want (using a lightweight pretrained model; clearly marked as “approximate”) • Parallel folder of cropped face thumbnails (aligned) for visual checks • README + environment setup + rerun instructions

@adriank06

Hi. I'm currently a masters student in field of AI and soft computing. my previous projects were in mostly in field of image processing, segmentation and detection. Here is a list of projects I have done so far : - segmentation and classification of MS disease cells from brain fmri images using MONAI and pytorch framework - white blood cell detection and classification using YOLO and vision Transformer based deep learning models. ( Open CV was used in this project ) - breast cancer cell segmentation using CNN based deep learning models using pytorch in neuromatch academy - I was also in a neuroscience research project related to image processing based on your project description, input data would have the challenge of having different angles, noise and different light sources. In this case having more rows of Data ( i.e. more images ) will be quite useful to achieve best accuracy though there are alternatives such as data augmentation techniques which I'll discuss later. I would love to have a meeting with you to discuss the technical aspects of the project since as long as I gather more information on the project and Data, I would deliver and deploy the product with more reliability and robustness.

@fatimamahsud1

Hi, I can build a Python-based AI pipeline to scan your personal photo albums, detect faces, and provide structured insights. The solution will: Process all images in a folder and detect faces using a robust model like MTCNN or RetinaFace. Output a JSON/CSV file per image with face counts, frequency per person, and optional basic demographics (age group, emotion). Save cropped face thumbnails in a separate folder for visual inspection. Include a simple command-line interface and clear README with setup instructions. I have experience with OpenCV, PyTorch, and TensorFlow, and can ensure high accuracy even with smartphone-style photos in varied lighting. The script will run on Windows and is easily extendable for new folders. I can deliver a working prototype quickly with clean, commented code.

@adinanipavan

Hi, I can build a clean, Windows-friendly face analysis pipeline that scans your personal photo folders, detects faces accurately, and exports structured data ready for analysis. For personal-album style images (smartphone shots, varied lighting), I would use a strong pre-trained detector such as RetinaFace or MTCNN combined with PyTorch/OpenCV for reliable performance. The focus will be minimizing false positives while keeping detection robust across casual photos. Proposed Approach • Folder-based batch processing from command line • Face detection + face cropping • Per-image metadata: face count, bounding boxes • Optional lightweight demographic estimation (age group, basic emotion classification using pre-trained models) • Structured output in JSON + CSV • Cropped face thumbnails saved in a parallel directory Output Example • CSV: image_name, face_count, age_group_estimates, emotion_tags • JSON: detailed per-face metadata • Thumbnails folder organized by source image The final solution will: • Run locally on Windows • Be triggered via simple CLI command • Include clean logging • Be fully documented in a README • Be easy to rerun on new folders I’ve worked on computer vision pipelines involving object detection and structured dataset generation, so this fits well with my experience. Ready to start and deliver a clean, analysis-ready solution. Pavan Kumar A

@atulgupta0

Hello, This is a clearly defined and practical computer vision project, and I can build a reliable end-to-end face analytics pipeline that meets all your requirements. I have strong experience developing deep learning systems for real-world image collections, including smartphone photos with varied lighting, angles, and image quality. Your use case — scanning large personal photo folders, detecting faces, extracting insights, and producing structured outputs — is exactly the type of production-grade AI workflow I specialize in. I will implement a robust pipeline that: • Recursively scans your input folders and processes all images safely • Detects faces using high-accuracy pretrained models (RetinaFace or MTCNN) with filtering to minimize false positives • Crops and saves face thumbnails for visual verification • Generates face embeddings to group recurring individuals and compute frequency • Predicts age group and emotion (where detectable) • Produces clean, analytics-ready JSON and CSV outputs • Runs from a simple Windows command-line interface You will receive fully working code, structured outputs, organized face thumbnails, and clear documentation explaining setup and reuse on new folders. The system will be modular, efficient, and designed for large personal photo collections — not just a demo prototype. Happy to discuss dataset size, hardware setup, and output format preferences before starting. thank you for your attention in this matter.

@InjectAI

I can build a reliable AI-based face detection pipeline for your personal photo albums. I have developed multiple similar AI-powered solutions for personal and commercial datasets, giving me expertise in accurate face detection and structured analysis. .................. What I Will Deliver .................. Python script or notebook to process any folder of images Per-image CSV/JSON with face count and optional demographics (age group, emotion) Cropped face thumbnails for quick verification Minimal false positives using MTCNN or RetinaFace with post-processing README with setup and usage instructions .................. Tech Stack .................. Python 3, OpenCV / Pillow, PyTorch or TensorFlow MTCNN / RetinaFace for detection NumPy / Pandas for data handling Optional: Seaborn / Matplotlib for visual analytics I offer introductory rates and guarantee a maintainable, production-ready solution. My portfolio includes multiple similar AI projects available on my LinkedIn profile. Regards, Malik Abdul Salam AI & Computer Vision Specialist

@sakethmallojjala

I have strong experience building computer vision pipelines using PyTorch, OpenCV, and pre-trained face detection models such as RetinaFace and MTCNN. I can develop a robust Windows-compatible command-line tool that scans your image folders, detects faces with minimal false positives, generates structured JSON/CSV outputs, and stores cropped face thumbnails for validation. I will ensure clean data structuring for downstream analysis and provide a clear README for reproducibility and future runs.

@PremchandGat

Hi, This is a well-defined and technically interesting project—I’d be glad to build this for you. I am an AI/ML researcher in a Tier 1 institute and I’d love to develop a clean, Windows-ready face analysis pipeline using PyTorch + RetinaFace (or MTCNN where appropriate) optimized for personal smartphone photos in varied lighting. The system will: • Scan any input folder recursively • Detect and count faces per image • Minimize false positives with confidence filtering + post-processing • Optionally estimate age-group and emotion using a lightweight pre-trained classifier • Export structured JSON/CSV summaries for easy Python analysis • Save cropped face thumbnails in a parallel directory for quick visual verification The final deliverable will include: – Clean, modular code for future extensions – Sample output files – A clear README with step-by-step Windows setup (Conda/venv supported) I focus on reliability, reproducibility, and clean data structures so your output is immediately usable for deeper analytics. Happy to discuss any preferences on model choice or demographic tagging depth before we begin. Best regards