Building a Face Detection System for Production

Building a Face Detection System for Production

Face detection is the first and critical step in any face pipeline. The task is to find all faces in an image and return bounding boxes with confidence scores. At first glance, it seems simple, but real-world conditions—small faces at a distance, profile angles, partial occlusions, poor lighting, masks—turn it into a non-trivial engineering challenge. In production systems, failing to detect even one face can critically degrade the entire pipeline's quality. For example, in stadium video surveillance, faces 20×20 pixels in size are the norm. Without specialized optimization, such objects are missed in 40% of frames. We develop detectors that work in production with latency as low as 4 ms on GPU while maintaining high accuracy even on complex scenes.

Why Standard Detectors Often Fail

Most open-source detectors are trained on datasets like WiderFace, where faces are well-lit and large. In reality, surveillance cameras, outdoor conditions, masks, and glasses reduce accuracy to 60–70%. We solve this by fine-tuning on target data with augmentations that simulate real conditions—rotations, shadows, blur. For example, adding synthetic masks during fine-tuning improves AP from 65% to 89% on the MAFA dataset.

How We Solve Face Detection

We use three main approaches depending on requirements.

SCRFD (Sample and Computation Redistribution for Face Detection) — currently the best speed/quality trade-off. SCRFD-10GF achieves 95.2% AP on WiderFace Hard, which is 2x faster than RetinaFace-R50 with comparable accuracy. More details can be found in the InsightFace repository.

RetinaFace — a classic with landmark detection (5 points: eyes, nose, mouth corners). Used for alignment before face recognition.

YOLOv8 fine-tuned on WiderFace — a versatile option for custom requirements.

from insightface.app import FaceAnalysis
import cv2

# InsightFace: detection + landmark detection
app = FaceAnalysis(allowed_modules=['detection'])
app.prepare(ctx_id=0, det_size=(640, 640))

def detect_faces(image_path: str) -> list[dict]:
    img = cv2.imread(image_path)
    faces = app.get(img)

    results = []
    for face in faces:
        results.append({
            'bbox': face.bbox.astype(int).tolist(),     # [x1, y1, x2, y2]
            'confidence': float(face.det_score),
            'landmarks': face.kps.astype(int).tolist()  # 5 keypoints
        })
    return results

Small Face Detection

Standard detectors miss faces smaller than 16×16 pixels. For surveillance cameras with large distances:

• Image tiling: split the image into overlapping tiles, detect on each, merge results via NMS
• SAHI (Slicing Aided Hyper Inference) — automatic tiling with merge. Library available on GitHub.

from sahi import AutoDetectionModel
from sahi.predict import get_sliced_prediction

model = AutoDetectionModel.from_pretrained(
    model_type='yolov8',
    model_path='face_detector.pt',
    confidence_threshold=0.3
)

result = get_sliced_prediction(
    image='crowd.jpg',
    detection_model=model,
    slice_height=512,
    slice_width=512,
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2
)

Performance on Different Hardware

How to Fine-Tune a Model for Masked Face Detection?

The pandemic created a separate class of tasks—detecting faces with medical masks. The MAFA dataset contains 35,806 annotated masked faces. Fine-tuning a standard detector on MAFA+WiderFace: AP on masked faces improves from 65% to 89%. The fine-tuning process includes:

1. Collecting or generating synthetic data with masks
2. Augmentation: rotations, lighting changes, blur
3. Fine-tuning a pre-trained model on the mixed dataset
4. Validation on a separate test set

This ensures stable operation with masks, glasses, and other occlusions.

What's Included in Our Face Detection Service

We provide a turnkey solution, including:

• Analysis of your conditions and preparation of synthetic/real data
• Selection and fine-tuning of the detector (SCRFD/RetinaFace/YOLOv8)
• Latency and memory footprint optimization (INT8 quantization, ONNX Runtime)
• Integration into your pipeline (REST API, gRPC, RTSP)
• Documentation and training for your team
• Support during operation

With 5 years of experience in computer vision, we have completed over 30 face detection and recognition projects. We process up to 100 FPS on a single GPU. Results are guaranteed—if accuracy does not meet targets, we refine at no extra cost.

Development Timelines

Request a demo version of the detector for your data and get a preliminary estimate within 1 day. Contact us to discuss your case.