1.1 Face Recognition >november_20_first day >09:00-18:00 >november_20_first day >09:00-18:00 >date/november 20-21 >place/grand nikko tokyo Entrance Take card out Tag card Face recognition Entrance Choose Floor Choose Floor Take card back 1. Face recognition replaces authentication 2. 1:1, Verification Elevator
1.1 Face Recognition >november_20_first day >09:00-18:00 >november_20_first day >09:00-18:00 >date/november 20-21 >place/grand nikko tokyo 1. Face recognition replaces payment 2. 1:N, Recognition Vending Machine Choose product Take cash out Put cash Face recognition Choose product Get Product Get Product Take change back
1.1 Face Recognition Before recognition Take card out After recognition Take card or change back Face recognition Before recognition After recognition Face recognition After recognition Before recognition Process with physical payment method 1. Simplify UX using face recognition 2. Insert second-verification flow when result is risky Wrap-Up
1.2 Face Check-in 1. Long lines of developers waiting to see keynotes 2. Personal information verification process by e-mail and name Check-in System Online Registration Line Up Check Personal Information Get Goods Watch Session Increased bottlenecks
1.2 Face Check-in 1. Reduction of waiting time with less than 1 second recognition speed 2. Remove process to verify name or personal information Check-in System Online Registration Line Up Face Recognition Get Goods Watch Session Reduced bottlenecks
Face Recognition Pipeline Face Identification 2) Get a shortlist for the probe 3) Recognizing identity via simple thresholding Gallery (A Set of face features) Probe ( A face feature ) 1) Nearest-Neighbor Retrieval It’s face feature, not a face image.!! It’s face feature, not a face image.!!
Applying Face Recognition to Service Support Multiplatform High Accuracy Fast Speed Deep Learning Framework Face Detection Face Alignment Face Recognition TensorFlow CoreML MLKit iOS Vision Framework iOS Android MacOS Windows Linux 99% Accuracy 0.1 Second Speed
Face Engine …. …. …. User Applications Face Engine C++ Frontend Python Frontend …. Frontend Function Backend Framework Detection Facial Landmark Recognition Face Pose Smoother Tracking For FP32, INT8 (CPU) Android iOS Linux MacOS Windows Passing Passing Passing Passing Passing Supporting
Face Engine Input: Image Output: Face (people) information Detector & Tracking Pose Estimation Low-Resolution Image Resize High-Resolution Image Post Processing Alignment Recognition Feature Z X Y Human Info Bounding Box Facial Landmark Face Feature Euler Angle
Sharing Optimization Experience In Face Engine Lightweight model for high accuracy and fast Inference engine optimization for more speed Layer optimization for more, more speed more, more, more..?
2.1.1 Lightweight Deep Learning Model Trade off of accuracy and speed Https://Www.Researchgate.Net/Publication/328017644_Benchmark_Analysis_of_Representative_Deep_Neural_Network
2.1.2 Face Detector CPU Real-Time Vs GPU Real-Time +: Backbone network only ++: Backbone + head Model Latency [ms] Model size[MB] Input Device Pelee [1] 43.86+ ~5.00 320x320 iPhone8 (GPU) Ours 5.74+ 0.14++ 320x320 iPhone7 (CPU) [1] Robert J. Wang et. al, Pelee: A Real-Time Object Detection System on Mobile Devices, NeurIPS, 2018
2.1.2 Face Detector [1] S. Zhang et. al, FaceBoxes: A CPU Real-time Face Detector with High Accuracy, IJCB, 2017 Model mAP+ Latency [ms] Model size [MB] Input Device (engine) FaceBoxes[1] 96.0 21.40 3.83 320x240 Xeon (pytorch) Ours 96.0 22.50 0.29 320x240 Xeon (pytorch) Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz +: Mean averaged precision is a measure of how accurately the position of the object is detected. (The higher the better) Equal level of mAP 10x reduction in model size‑ FDDB ROC Curve The model located in upper left
2.1.3 Facial Landmark Detector Metric: Mean squared error Dataset: 3000W-test Model MSE(Fullset) DSRN[3] 5.21 SBR[1] 4.99 RCN-L+ELT-all[4] 4.90 PCD-CNN[2] 4.44 Ours (Teacher) 3.73 ResNet50+PDB+Wing[5] 3.60 LAB[0] 3.49 Lower is better [0] Look at Boundary: A Boundary-Aware Face Alignment Algorithm, CVPR, 2018 [1] Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors, CVPR, 2018 [2] Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment, CVPR, 2018 [3] Direct Shape Regression Networks for End-to-End Face Alignment, CVPR, 2018 [4] Improving Landmark Localization with Semi-Supervised Learning, CVPR, 2018 [5] Wing Loss for Robust Facial Landmark Localisation with Convolutional Nerual Netowrks, CVPR, 2018 [6] https://ibug.doc.ic.ac.uk/resources/300-W/ Latency(CPU) : 1.2 sec Model size : 93.4MB
2.1.4 Face Recognizer Baseline model : MobileFaceNet [1] Model mAP Latency [ms] Model size [MB] MobileFaceNet [1] 99.18 11.59 4.00 Ours 99.33 11.49 3.96 [1] MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices, arXiv preprint 1804.07573, 2018 Qualcomm Snapdragon 820 with 4 threads
2.2.1 Engine Performance Measurement Convolution VS Depthwise separable convolution 3x3 Convolution Input channel : 16 Output channel : 16 # of Params : 3 x 3 x 16 x 16 3x3 Depthwise Conv Input channel : 16 Output channel : 16 # of Params : 3 x 3 x 16 1x1 Pointwise Conv # of Params : 1 x 1 x 16 x 16 Total : (3 x 3 x 16) + (1 x 1 x 16 x 16)
2.2.1 Engine Performance Measurement Convolution VS Depthwise separable convolution Image : 10 x 10 x 3 Input channel : 3 Output channel : 3 Convolution [ 3 x 3 x 3 x 3] Total parameters : 81 (3x3x3x3) Total multiplications : 24300 (Image x Total Params) Depthwise convolution [ 3 x 3 x 3 x 3] # of Parameters : 27 (3x3x3) # of multiplications : 8100 (Image x Params) Pointwise convolution [ 1 x 1 x 3 x 3 ] # of parameters : 9 (1x1x3x3) # of multiplications : 2700 (Image x Params) Total parameters : 36 Total multiplications : 10800 3 x 3 x c x c > 3 x 3 x c + (c x c)
2.3.1 for Loop(Direct Convolution) Simply implemented - Overlapping multiple for-loops (with AVX/Neon) For i: = 1 to D For j := 1 to C For K := 1 to W For 1 := 1 to H For m := 1 to K*K …
2.3.2 GEMM-Based Convolution Conventional implementation VS Optimized implementation . . . K K C Convolution weights: D filters Feature map D x (K"C) (K"C) x N * Matrix multiply = Reshape im2col W H N ≈ (H x W) / (Stride) (Accelerated by optimized GEMM) # of multiplication : D x (%&') x N Memory Usage‐ K K C Convolution weights: D filters Feature map (K"C) cut into the vector size of CPU $ Multiply-and-Add = im2row W H (BLAS Free!) (K"C) cut into the vector size of CPU Repeat (D x N) times! # of multiplication : D x (%&') x N
2.3.3 Winograd Convolution # of multiplication : D x (!"#) x N Reference: Song Han, cs231n lecture note, Stanford University # of multiplication decreases! # of multiplication decreases!
2.4.2 Floating Point IEEE Standard 754 Floating Point Numbers 1bit 8bit 23bit Sign Bit Exponent Bit Fraction, Significant, Mantissa Bit N = (−1)s × (1.m) × 2(e−127)
2.4.2 Floating Point This value is eventually close to zero. The method we can take is to replace the subnormal number with the number of normalized express
2.4.2 Floating Point After Replacing the Values, the Accuracy of the Model Was Not Affected. However, Device Galaxy s7 Pixel3 Galaxy note 10+ iPhone7 Geekbench (Singlecore) 341 489 756 744 Model Latency [ms] 159.10 29.71 57.90 13.71 Replace Denormals [ms] 32.97 29.71 11.73 13.71 159.10 ms ➡ 32.97 ms (Up to 5x faster‐) 57.90 ms ➡ 11.73 ms (Up to 5x faster‐) ! !
3.1 Flowchart 1)Register with accurate face data 2)Recognition with minimal reference time 3)Seamless interact between server and client Registration Recognit : Client : Server Detect Landmark Recognize Detect Landmark Recognize
3.2 Client Detect Preprocessing Landmark Postprocessing User Recognition (Entrance) Face size Face Alignment Network Frame Processing Postprocess, UX Design and Development Preprocess, UX Design and Development Frame Processing Device type Device tilt
3.2.2 Landmark Postprocessing 3. Face Alignment 4. Euler Angle Face Status # compute the center of mass for each eye leftEyeCenter = leftEyePts.mean(axis=0).astype("int") rightEyeCenter = rightEyePts.mean(axis=0).astype("int") # compute the angle between the eye centroids dY = rightEyeCenter[1] - leftEyeCenter[1] dX = rightEyeCenter[0] - leftEyeCenter[0] angle = np.degrees(np.arctan2(dY, dX)) - 180 https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/ ▲ ▲ ✓ ✓
3.2.3 Speed Optimization Is the Result More Accurate Than the Threshold Value? Is the Detected Result Encrypted? Is the Recognized Person Same? Network 1. Consideration for encryption 2. Find optimal results in multi-requests Encrypt Recognized Face Encrypt Features Encrypt Network Encrypt Result
3.2.3 Speed Optimization 4. Network Size Optimization (change data type) Network [UInt8] Binary 15 times network API test 4.18 ms ➡ 2.60 ms (Up to 1.6x faster‐) 4.02 ms ➡ 2.41 ms (Up to 1.6x faster‐)
3.2.3 Speed Optimization 4. Network Size Optimization (gzip) Network Before 15 times network API test 2.60 ms ➡ 1.85 ms (Up to 1.7x faster‐) 2.41 ms ➡ 1.75 ms (Up to 1.7x faster‐) After
4.2 Service Optimization Detect preprocessing Landmark postprocessing Network Frame Processing Postprocess, UX Design and Development Face Alignment Face size Device tilt Device type Frame Processing Preprocess, UX Design and Development
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