AI Smart Mirror

@rajeshpachaikani

Hi, I'm Rajesh, mechatronics engineer and founder of Unartech Solutions, Coimbatore — practically your neighbour in Karur. Face-to-face at any milestone is easy. Face Recognition — OpenCV + ArcFace/DeepFace for single-user verification with very low false-accept rates. MediaPipe Face Mesh for landmark alignment on commodity webcams. Outfit Overlay — MediaPipe Pose anchors shoulder/hip landmarks; PNG layers warp to the torso in real time. Clean foundation to extend to 3-D later. Data Strategy — LFW + VGGFace2 for pre-training; a short on-device enrollment session (~10 photos → augmented to 500) for the target user; Dress Code / DeepFashion subsets for outfit PNGs. No licensing risk. Stack — Python 3.11 + OpenCV + MediaPipe + PyQt6, packaged with PyInstaller for clean Windows deployment. Milestones: Week 1 – Data + Baseline: ₹15k Week 2 – Face Recognition Demo: ₹25k Week 3 – Overlay + Polish: ₹15k Final – Testing + Handoff: ₹20k Happy to jump on a call this week. Rajesh Unartech Solutions, Coimbatore

@Hemantt841

I appreciate the detailed outline you’ve provided for ZENVIA’s AI-powered smart mirror, and I’m excited about the opportunity to develop the core software stack you need. Your focus on creating a highly accurate face recognition login and the real-time outfit overlay feature is clear and ambitious, especially under a tight 3–4 week timeline. Your requirement for a Windows application using Python 3.x with OpenCV and potentially MediaPipe to enhance landmark detection stands out, as does the challenge of sourcing or curating open datasets for both face recognition and outfit imagery without licensing issues. The modular overlay pipeline for PNG outfit layers aligned to the torso and the need for a lightweight, commodity hardware-compatible solution are also important details I’ve noted. I recently developed a Python-based face recognition system using OpenCV and deep learning models fine-tuned on curated datasets, tailored for a single-user environment with minimal false accepts. Additionally, I built an augmented reality try-on demo that overlays 2D images on live webcam feeds, optimizing for real-time performance on Windows. This experience directly aligns with your project’s core tasks and tech stack. I can deliver the initial data strategy and baseline model within the first week, have the integrated face recognition demo ready by week two, and complete the outfit overlay and polish by week three, leaving final days for testing and documentation. I’m open to discussing any adjustments to fit your milestones and ensure smooth collaboration.

@UzeeTech

With a focused expertise in Python, I offer precisely the skill set you are searching for to develop the core software stack for your AI-powered smart mirror. My experience spans over 7 fruitful years in the industry, during which I've had the privilege of working on numerous complex projects that demanded stringent attention to detail and technical mastery. This has honed my abilities to design and train robust AI models, an essential requirement for successfully executing this endeavor. Moreover, I have a solid background in computer vision and have worked extensively with OpenCV and MediaPipe, making me a natural fit for incorporating these technologies into your project. I recognize that sourcing or curating open-source data is crucial for a time-sensitive yet top-tier job like this and I assure you that I possess the right acumen to undertake this important task efficiently, without infringing any licenses. Overall, my track record reflects a disciplined approach towards meeting deadlines, maintaining smooth communication, delivering clean code and thinking holistically. From the inception of a concept to its successful deployment, I am known for providing comprehensive end-to-end solutions. Let's team up to create a revolutionary product exceeding the customer's expectations.

@kshubham102

Hello, I can help you build the core computer vision system for your AI smart mirror with a focus on accuracy, performance, and fast delivery. Approach: • Implement face recognition using OpenCV + MediaPipe/FaceNet for high-accuracy login (low false accept/reject rates) • Source and fine-tune open datasets (e.g., VGGFace2, DeepFashion) for reliable performance • Build a real-time outfit overlay system using body landmarks (MediaPipe) aligned to torso • Develop a lightweight Windows app (Python + PyQt) with smooth camera feed and interaction • Optimize for real-time performance on standard webcams Deliverables: • Full source code + pretrained models • Working Windows demo (face login + virtual try-on) • Short demo video + setup guide Timeline: 3–4 weeks (as per milestones) Price: ₹70,000 (milestone-based) I’ve worked on real-time CV pipelines and can ensure this prototype is both accurate and demo-ready within your timeline. I don't have relevant links but I can ensure you that in the 1st milestone itself you can see the accuracy. If not I will refund you amount as well. Best regards. Shubham Sharma

@kaustubhbawa123

Hi, I am an IIT Grad, PMP Certified Professional, ex-BFSI and worked at fortune 500 companies. I will make it a reality for you. As a Computer Vision Specialist, I will implement face recognition using OpenCV's Haar cascade classifiers and machine learning-based approaches like FaceNet or DeepFace to achieve high accuracy. Additionally, I can utilize MediaPipe for more advanced computer vision tasks like object detection and image segmentation to enhance the "try-on" feature. Kindly click on the chat button so we can discuss and get started. Will share you my prior projects done and my resume too. I have been doing freelancing since 2019 worked at top MNCs in both USA and India. Lets connect

@toptruelancer

Hi, I’m a Computer Vision Engineer with 7+ years of experience building real-time AI systems using Python, OpenCV, and MediaPipe. Your ZENVIA smart mirror aligns perfectly with my past work in face recognition and AR-based overlays. Why I’m a good fit: Built high-accuracy face recognition systems using FaceNet/ArcFace with low false accept rates Developed real-time body/face landmark pipelines using MediaPipe for AR applications Experience optimizing CV pipelines to run smoothly on standard webcams (30+ FPS) Strong background in rapid prototyping with clean, production-ready code How I’ll approach your project:

@gtm7272

You’re not building a demo—you’re defining the first real user moment for ZENVIA. That’s how we’ll approach it. At Wuagle, we specialize in turning complex AI ideas into fast, production-ready systems. This project sits right at the intersection of computer vision, real-time processing, and UX—our core strength. For face recognition, we’ll use a lightweight embedding model (ArcFace/FaceNet class) optimized for a single-user device. This lets us push accuracy high while keeping latency near-instant, with calibrated thresholds to minimize false accepts/rejects. For virtual try-on, we’ll build a modular OpenCV + MediaPipe pipeline: torso landmark detection, scale/alignment normalization, and smooth real-time overlay rendering on standard hardware. Data won’t slow us down—we’ll combine permissive open datasets, synthetic augmentation, and a quick personalization loop to reach production-quality safely. Execution: Week 1: Data + face recognition baseline Week 2: Windows app + real-time login Week 3: Overlay system + performance tuning Final: Testing + handoff You’ll get clean code, pretrained models, a working GUI, and a system designed to scale—not rebuild. We’ve delivered similar CV systems and award-winning AI work with Samsung integrations. If speed + reliability matter, we should start now.

@Sh4h11d

Hi, I can help you build the ZENVIA AI smart mirror prototype with a strong focus on accuracy and real-time performance. I’ll develop a face recognition system using Python, OpenCV, and deep-learning embeddings, optimized for single-user login with very low false results. I can also integrate MediaPipe for fast detection and tracking. For the virtual try-on, I’ll create a real-time overlay system that aligns outfit images to the user’s torso using pose landmarks, keeping it smooth and extensible. I’ll also assist with sourcing/license-safe datasets and deliver a clean Windows app (PyQt), along with code, trained models, demo video, and documentation. I can complete this within your 3–4 week timeline. Looking forward to working with you. Best regards

@ashiqiitkgp

Hi, As an AI Ph.D. researcher specializing in deep learning and CPU-GPU co-design, I will build your lightweight AI Smart Mirror prototype for Windows to run flawlessly on standard hardware. My 4-week plan: Face Recognition: Curate open datasets (LFW) and build a secure login pipeline using OpenCV and facial embeddings (ArcFace) for near-zero false accepts. Try-On Overlay: Integrate MediaPipe for pose detection, dynamically anchoring 2D outfit PNGs to the torso at interactive frame rates. GUI: Wrap the logic in a clean PyQt interface, optimizing for real-time inference. My highly competitive bid is INR 45,000: • Wk 1 (INR 10k): Data curation & baseline model. • Wk 2 (INR 12.5k): Face recognition Windows GUI demo. • Wk 3 (INR 12.5k): MediaPipe pose & 2D outfit overlay. • Wk 4 (INR 10k): Testing, docs, & final hand-off. I'll deliver source code, pre-trained models, docs, and a video demo. Let's connect to begin immediately!