diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index de2f38a..35e4f5b 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -3,6 +3,7 @@ import warnings
 warnings.filterwarnings("ignore")
 import time
 import os
+from os import path
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
@@ -11,6 +12,7 @@ import cv2
 from keras import backend as K
 import keras
 import tensorflow as tf
+import pickle
 
 #from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
 #from extendedmodels import Age, Gender, Race, Emotion
@@ -327,7 +329,137 @@ def detectFace(img_path):
 	img = functions.detectFace(img_path)[0] #detectFace returns (1, 224, 224, 3)
 	return img[:, :, ::-1] #bgr to rgb
 
-
+def find(img_path, db_path
+	, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True):
+		
+	tic = time.time()
+	
+	if type(img_path) == list:
+		bulkProcess = True
+		img_paths = img_path.copy()
+	else:
+		bulkProcess = False
+		img_paths = [img_path]
+	
+	if os.path.isdir(db_path) == True:
+		
+		#---------------------------------------
+		
+		if model == None:
+			if model_name == 'VGG-Face':
+				print("Using VGG-Face model backend and", distance_metric,"distance.")
+				model = VGGFace.loadModel()
+			elif model_name == 'OpenFace':
+				print("Using OpenFace model backend", distance_metric,"distance.")
+				model = OpenFace.loadModel()
+			elif model_name == 'Facenet':
+				print("Using Facenet model backend", distance_metric,"distance.")
+				model = Facenet.loadModel()
+			elif model_name == 'DeepFace':
+				print("Using FB DeepFace model backend", distance_metric,"distance.")
+				model = FbDeepFace.loadModel()
+			else:
+				raise ValueError("Invalid model_name passed - ", model_name)	
+		else: #model != None
+			print("Already built model is passed")
+		
+		input_shape = model.layers[0].input_shape[1:3]
+		threshold = functions.findThreshold(model_name, distance_metric)
+		
+		#---------------------------------------
+		
+		file_name = "representations_%s.pkl" % (model_name)
+		file_name = file_name.replace("-", "_").lower()
+		
+		if path.exists(db_path+"/"+file_name):
+			
+			print("WARNING: Representations for images in ",db_path," folder were previously stored in ", file_name, ". If you added new instances after this file creation, then please delete this file and call find function again. It will create it again.")
+			
+			f = open(db_path+'/'+file_name, 'rb')
+			representations = pickle.load(f)
+			
+			print("There are ", len(representations)," representations found in ",file_name)
+			
+		else:
+			employees = []
+			
+			for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
+				for file in f:
+					if ('.jpg' in file):
+						exact_path = r + "/" + file
+						employees.append(exact_path)
+			
+			if len(employees) == 0:
+				raise ValueError("There is no image in ", db_path," folder!")
+			
+			#------------------------
+			#find representations for db images
+			
+			representations = []
+			
+			pbar = tqdm(range(0,len(employees)), desc='Finding representations')
+			
+			#for employee in employees:
+			for index in pbar:
+				employee = employees[index]
+				img = functions.detectFace(employee, input_shape, enforce_detection = enforce_detection)
+				representation = model.predict(img)[0,:]
+				
+				instance = []
+				instance.append(employee)
+				instance.append(representation)
+				
+				representations.append(instance)
+			
+			f = open(db_path+'/'+file_name, "wb")
+			pickle.dump(representations, f)
+			f.close()
+			
+			print("Representations stored in ",db_path,"/",file_name," file. Please delete this file when you add new identities in your database.")
+		
+		#----------------------------
+		#we got representations for database
+		df = pd.DataFrame(representations, columns = ["identity", "representation"])
+		df_base = df.copy()
+		
+		resp_obj = []
+		
+		global_pbar = tqdm(range(0,len(img_paths)), desc='Analyzing')
+		for j in global_pbar:
+			img_path = img_paths[j]
+		
+			#find representation for passed image
+			img = functions.detectFace(img_path, input_shape, enforce_detection = enforce_detection)
+			target_representation = model.predict(img)[0,:]
+		
+			distances = []
+			for index, instance in df.iterrows():
+				source_representation = instance["representation"]
+				distance = dst.findCosineDistance(source_representation, target_representation)
+				distances.append(distance)
+		
+			df["distance"] = distances
+			df = df.drop(columns = ["representation"])
+			df = df[df.distance <= threshold]
+		
+			df = df.sort_values(by = ["distance"], ascending=True).reset_index(drop=True)
+			resp_obj.append(df)
+			df = df_base.copy() #restore df for the next iteration
+			
+		toc = time.time()
+		
+		print("find function lasts ",toc-tic," seconds")
+		
+		if len(resp_obj) == 1:
+			return resp_obj[0]
+		
+		return resp_obj
+		
+	else:
+		raise ValueError("Passed db_path does not exist!")
+		
+	return None
+	
 def stream(db_path = '', model_name ='VGG-Face', distance_metric = 'cosine', enable_face_analysis = True):
 	realtime.analysis(db_path, model_name, distance_metric, enable_face_analysis)