---
# System prepended metadata

title: 測試 similarity - 內建

---

# Human 測試 similarity - 內建

## 使用 ROC

接下來套用模板, 混淆矩陣

### model - threshold

| 真同個人  | 偽同個人 | 
| -------- | ------ |
| 偽不同人  | 真不同人 |

* TPR: 
* FPR: 
* ACC: 
* ave ACC:
PS: (真同個人 / 實際同個人 + 真不同人 / 實際不同人) / 2
* 除了列出 0.4 - 0.9
會嘗試找促使 ACC, ave ACC 較高的 threshold

---

### HSE FaceRes(Deep) - 0.4

| 1210     | 3596   |
| -------- | ------ |
| 0        | 4      |

* TPR: 100 %
* FPR: 99.89 %
* ACC: 25.24 %

### HSE FaceRes(Deep) - 0.5

| 1209     | 3475   |
| -------- | ------ |
| 1        | 125    |

* TPR: 99.92 %
* FPR: 96.53 %
* ACC: 27.73 %

### HSE FaceRes(Deep) - 0.6

| 1163     | 1866   |
| -------- | ------ |
| 47       | 1734   |

* TPR: 96.12 %
* FPR: 51.83 %
* ACC: 60.23 %
* ave ACC: 72.14 %

### HSE FaceRes(Deep) - 0.64

| 1041     | 806    |
| -------- | ------ |
| 169      | 2794   |

* TPR: 86.03 %
* FPR: 22.39 %
* ACC: 79.73 %
* ave ACC: 81.82 %

### HSE FaceRes(Deep) - 0.65

| 984      | 590    |
| -------- | ------ |
| 226      | 3010   |

* TPR: 81.32 %
* FPR: 16.39 %
* ACC: 83.04 %
* ave ACC: 82.47 %

### HSE FaceRes(Deep) - 0.67

| 984      | 252    |
| -------- | ------ |
| 376      | 3348   |

* TPR: 68.93 %
* FPR: 7.00 %
* ACC: 86.94 %
* ave ACC: 80.96 %

### HSE FaceRes(Deep) - 0.68

| 752      | 138    |
| -------- | ------ |
| 458      | 3462   |

* TPR: 62.15 %
* FPR: 3.83 %
* ACC: 87.61 %
* ave ACC: 79.16 %

### HSE FaceRes(Deep) - 0.69

| 657      | 76    |
| -------- | ------ |
| 553      | 3524   |

* TPR: 54.30 %
* FPR: 2.11 %
* ACC: 86.92 %

### HSE FaceRes(Deep) - 0.7

| 553      | 38     |
| -------- | ------ |
| 657      | 3562   |

* TPR: 45.70 %
* FPR: 1.06 %
* ACC: 85.55 %

### HSE FaceRes(Deep) - 0.8

| 39       | 0      |
| -------- | ------ |
| 1171     | 3600   |

* TPR: 3.22 %
* FPR: 0 %
* ACC: 75.65 %

### HSE FaceRes(Deep) - 0.9

| 3        | 0      |
| -------- | ------ |
| 1207     | 3600   |

* TPR: 0.25 %
* FPR: 0 %
* ACC: 74.91 %

### ROC 圖(點旁的標註為 threshold)

藍色: highest ave ACC
綠色: highest ACC

![HSE_FaceRes_Deep_ROC](https://hackmd.io/_uploads/B1a6ebfmA.png)

ave detect single image: 102.58 ms


---

### HSE FaceRes - 0.4

| 1190     | 1467   |
| -------- | ------ |
| 20       | 2133   |

* TPR: 98.35 %
* FPR: 40.75 %
* ACC: 69.09 %
* ave ACC: 78.80 %

### HSE FaceRes - 0.44

| 1128     | 549    |
| -------- | ------ |
| 82       | 3051   |

* TPR: 93.22 %
* FPR: 15.25 %
* ACC: 86.88 %
* ave ACC: 88.99 %

### HSE FaceRes - 0.45

| 1090     | 403    |
| -------- | ------ |
| 120      | 3197   |

* TPR: 90.08 %
* FPR: 11.19 %
* ACC: 89.13 %
* ave ACC: 89.44 %

### HSE FaceRes - 0.47

| 987      | 220    |
| -------- | ------ |
| 223      | 3380   |

* TPR: 81.57 %
* FPR: 6.11 %
* ACC: 90.79 %
* ave ACC: 87.73 %

### HSE FaceRes - 0.48

| 924      | 135    |
| -------- | ------ |
| 286      | 3465   |

* TPR: 76.36 %
* FPR: 3.75 %
* ACC: 91.25 %
* ave ACC: 86.31 %

### HSE FaceRes - 0.49

| 862      | 91     |
| -------- | ------ |
| 348      | 3509   |

* TPR: 71.24 %
* FPR: 2.53 %
* ACC: 90.87 %
* ave ACC: 84.36 %


### HSE FaceRes - 0.5

| 795      | 63     |
| -------- | ------ |
| 415      | 3537   |

* TPR: 65.70 %
* FPR: 1.75 %
* ACC: 90.06 %
* ave ACC: 81.98 %

### HSE FaceRes - 0.6

| 208      | 0      |
| -------- | ------ |
| 1002     | 3600   |

* TPR: 17.19 %
* FPR: 0 %
* ACC: 79.17 %
* ave ACC: 58.60 %

### HSE FaceRes - 0.7

| 41       | 0      |
| -------- | ------ |
| 1169     | 3600   |

* TPR: 3.42 %
* FPR: 0 %
* ACC: 75.70 %

### HSE FaceRes - 0.8

| 4        | 0      |
| -------- | ------ |
| 1206     | 3600   |

* TPR: 0.33 %
* FPR: 0 %
* ACC: 74.93 %

### HSE FaceRes - 0.9

| 2        | 0      |
| -------- | ------ |
| 1208     | 3600   |

* TPR: 0.17 %
* FPR: 0 %
* ACC: 74.89 %

### ROC 圖(點旁的標註為 threshold)

藍色: highest ave ACC
綠色: highest ACC

![HSE_FaceRes_ROC](https://hackmd.io/_uploads/ByoMixMXR.png)

ave detect single image: 104.17 ms

---

### mobileface - 0.4/0.5/0.6/0.8/0.9

| 1210     | 3600   |
| -------- | ------ |
| 0        |  0     |

* TPR: 100 %
* FPR: 100 %
* ACC: 25.16 %

### mobileface - 0.95

| 1141     | 3326   |
| -------- | ------ |
| 69       | 274    |

* TPR: 94.30 %
* FPR: 92.39 %
* ACC: 29.42 %
* ave ACC: 50.95 %

### mobileface - 0.96

| 891      | 2249   |
| -------- | ------ |
| 319      | 1351   |

* TPR: 73.64 %
* FPR: 62.47 %
* ACC: 46.61 %
* ave ACC: 55.58 %

### mobileface - 0.97

| 434      | 666    |
| -------- | ------ |
| 776      | 2934   |

* TPR: 35.87 %
* FPR: 18.50 %
* ACC: 70.02 %
* ave ACC: 58.68 %

### mobileface - 0.98

| 65       | 27     |
| -------- | ------ |
| 1145     | 3573   |

* TPR: 5.37 %
* FPR: 0.75 %
* ACC: 75.63 %
* ave ACC: 52.31 %

### mobileface - 0.99

| 0        | 0      |
| -------- | ------ |
| 1210     | 3600   |

* TPR: 0 %
* FPR: 0 %
* ACC: 74.84 %
* ave ACC: 50.00 %

### ROC 圖(點旁的標註為 threshold)

藍色: highest ave ACC
綠色: highest ACC

![mobileface_ROC](https://hackmd.io/_uploads/ryBnT4f7R.png)

ave detect single image: 82.40 ms


---

### mobilefacenet - 0.4/0.5/0.6/0.8/0.9

| 1210     | 3600   |
| -------- | ------ |
| 0        |  0     |

* TPR: 100 %
* FPR: 100 %
* ACC: 25.16 %

### mobilefacenet - 0.998

| 1210     | 3487   | 
| -------- | ------ |
| 0        | 113    |

* TPR: 100 %
* FPR: 96.86 %
* ACC: 27.51 %
* ave ACC: 51.57 % 

### mobilefacenet - 0.9985

| 1187     | 2983   | 
| -------- | ------ |
| 23       | 617    |

* TPR: 98.10 %
* FPR: 82.86 %
* ACC: 37.51 %
* ave ACC: 57.62 %

### mobilefacenet - 0.999

| 941      | 1663   | 
| -------- | ------ |
| 269      | 1937   |

* TPR: 77.77 %
* FPR: 46.19 %
* ACC: 59.83 %
* ave ACC: 65.79 %

### mobilefacenet - 0.9993

| 534      | 660    | 
| -------- | ------ |
| 676      | 2940   |

* TPR: 44.13 %
* FPR: 18.33 %
* ACC: 59.83 %
* ave ACC: 62.90 %

### mobilefacenet - 0.9995

| 251      | 201    |
| -------- | ------ |
| 959      | 3399   |

* TPR: 20.74 %
* FPR: 5.58 %
* ACC: 75.88 %
* ace ACC: 57.58 %

### mobilefacenet - 0.9997

| 43       | 28     |
| -------- | ------ |
| 1167     | 3572   |

* TPR: 3.55 %
* FPR: 0.78 %
* ACC: 75.16 %
* ace ACC: 51.39 %

### ROC 圖(點旁的標註為 threshold)

藍色: highest ave ACC
綠色: highest ACC

![mobilefacenet_ROC](https://hackmd.io/_uploads/rJKdpVG7C.png)

ave detect single image: 87.03