C# 30分钟完成百度人脸识别——进阶篇（文末附源码）（下）

//人脸识别
        public JsonResult Face_Distinguish()
        {
            // 设置APPID/AK/SK
            var API_KEY = "XFPA49myCG7S37XP1DxjLbXF";                   //你的 Api Key
            var SECRET_KEY = "ZvZKigrixMLXNZOLmkrG6iDx9QprlGuT";        //你的 Secret Key
            var client = new Baidu.Aip.Face.Face(API_KEY, SECRET_KEY);
            client.Timeout = 60000;  // 修改超时时间
            var imageType = "BASE64";  //BASE64   URL
            string imgData64 = Request["imgData64"];
            imgData64 = imgData64.Substring(imgData64.IndexOf(",") + 1);      //将‘，’以前的多余字符串删除
            ResultInfo result = new ResultInfo();
            try
            {
                var groupId = "group1";
                var userId = "user1";
                var result323 = client.Detect(imgData64, imageType);
                //活体检测阈值是多少
                //0.05 活体误拒率：万分之一；拒绝率：63.9%
                //0.3 活体误拒率：千分之一；拒绝率：90.3%
                //0.9 活体误拒率：百分之一；拒绝率：97.6%
                //1误拒率: 把真人识别为假人的概率. 阈值越高，安全性越高, 要求也就越高, 对应的误识率就越高
                //2、通过率=1-误拒率
                //所以你thresholds参数返回 和 face_liveness 比较大于推荐值就是活体
                ////活体判断
                var faces = new JArray
                        {
                            new JObject
                            {
                                {"image", imgData64},
                                {"image_type", "BASE64"}
                            }
                        };
                var Living = client.Faceverify(faces);  //活体检测交互返回
                var LivingJson = Newtonsoft.Json.JsonConvert.SerializeObject(Living);
                var LivingObj = Newtonsoft.Json.JsonConvert.DeserializeObject(LivingJson) as JObject;
                if (LivingObj["error_code"].ToString() == "0" && LivingObj["error_msg"].ToString() == "SUCCESS")
                {
                    var Living_result = Newtonsoft.Json.JsonConvert.DeserializeObject(LivingObj["result"].ToString()) as JObject;
                    var Living_list = Living_result["thresholds"];
                    double face_liveness = Convert.ToDouble(Living_result["face_liveness"]);
                    var frr = Newtonsoft.Json.JsonConvert.SerializeObject(Living_list.ToString());
                    var frr_1eObj = Newtonsoft.Json.JsonConvert.DeserializeObject(Living_list.ToString()) as JObject;
                    double frr_1e4= Convert.ToDouble(frr_1eObj["frr_1e-4"]);
                    if (face_liveness < frr_1e4)
                    {
                        result.info = "识别失败：不是活体！";
                        return Json(result, JsonRequestBehavior.AllowGet);
                    }
                }
                //首先查询是否存在人脸
                var result2 = client.Search(imgData64, imageType, groupId);  
                var strJson = Newtonsoft.Json.JsonConvert.SerializeObject(result2);
                var o2 = Newtonsoft.Json.JsonConvert.DeserializeObject(strJson) as JObject;
                //判断是否存在当前人脸，相识度是否大于80
                if (o2["error_code"].ToString() == "0" && o2["error_msg"].ToString() == "SUCCESS")
                {
                    var result_list = Newtonsoft.Json.JsonConvert.DeserializeObject(o2["result"].ToString()) as JObject;
                    var user_list = result_list["user_list"];
                    var Obj = JArray.Parse(user_list.ToString());
                    foreach (var item in Obj)
                    {
                        //80分以上可以判断为同一人，此分值对应万分之一误识率
                        var score = Convert.ToInt32(item["score"]);
                        if (score > 80)
                        {
                            result.info = result2.ToString();
                            result.res = true;
                            result.startcode = 221;
                            return Json(result, JsonRequestBehavior.AllowGet);
                        }
                    }
                }
                else
                {
                    result.info = strJson.ToString();
                    result.res = false;
                    return Json(result, JsonRequestBehavior.AllowGet);
                }
            }
            catch (Exception e)
            {
                result.info = e.Message;
            }
            return Json(result, JsonRequestBehavior.AllowGet);
        }
        //识别成功，查询数据库
        public JsonResult Face_UserInfoList()
        {
            ResultInfo result = new ResultInfo();
            //这里就不进行非空判断了，后期根据实际情况进行优化
            var Guid_Id = Request["Guid_Id"];
            //根据人脸唯一标识判断是否存在数据
            List<Face_UserInfo> strlist = new Face_UserInfoBLL().GetfaceinfoByToken(Guid_Id);
            var strJson = Newtonsoft.Json.JsonConvert.SerializeObject(strlist);
            result.info = strJson;
            result.res = true;
            return Json(result, JsonRequestBehavior.AllowGet);
        }